cs 6601 assignment 3 bayes nets

These [slides](https://www.cs.cmu.edu/~scohen/psnlp-lecture6.pdf) provide a nice intro, and this [cheat sheet](http://www.bcs.rochester.edu/people/robbie/jacobslab/cheat_sheet/MetropolisHastingsSampling.pdf) provides an explanation of the details. This page constitutes my learning portfolio for CS 6601, Artificial Intelligence, taken in Fall 2012. Due Thursday Oct 29th at 7:00 pm. # Assume that the following statements about the system are true: # 1. Lab Assignment 3 (10 marks). # Each team can either win, lose, or draw in a match. CS 344 and CS 386 are core courses in the CSE undergraduate programme. Reading: Pieter Abbeel's introduction to Bayes Nets. # A_distribution = DiscreteDistribution(A), # index = A_distribution.generate_index([],[]), # If you wanted to set the distribution for P(A|G) to be, # dist = zeros([G_node.size(), A.size()], dtype=float32), # A_distribution = ConditionalDiscreteDistribution(nodes=[G_node,A], table=dist), # Modeling a three-variable relationship is a bit trickier. # Note: Just measure how many iterations it takes for Gibbs to converge to a stable distribution over the posterior, regardless of how close to the actual posterior your approximations are. – Example : P(H=y, F=y) = 2/8 • Could encode this into a table: ... • Bayes’ nets can solve this problem by exploiting independencies. # 1d: Probability calculations : Perform inference. """Create a Bayes Net representation of the game problem. This is a collection of assignments from OMSCS 6601 - Artificial Intelligence. ', 'No, because its underlying undirected graph is not a tree. There are also plenty of online courses on “How to do AI in 3 hours” (okay maybe I’m exaggerating a bit, it’s How to do AI in 5 hours). CS 343H: Honors Artificial Intelligence Bayes Nets: Inference Prof. Peter Stone — The University of Texas at Austin [These slides based on those of Dan Klein and Pieter Abbeel for … # Hint 1: in both Metropolis-Hastings and Gibbs sampling, you'll need access to each node's probability distribution and nodes. # TODO: write an expression for complexity. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. # Design a Bayesian network for this system, using pbnt to represent the nodes and conditional probability arcs connecting nodes. Assignments 3-6 don't get any easier. We use analytics cookies to understand how you use our websites so we can make them better, e.g. I will be updating the assignment with questions (and their answers) as they are asked. 10-601 Machine Learning, Fall 2011: Homework 3 Machine Learning Department Carnegie Mellon University Due: October 17, 5 PM Instructions There are 3 questions on this assignment. Probabilistic Inference ! # You will test your implementation at the end of the section. # 2. You can always update your selection by clicking Cookie Preferences at the bottom of the page. Returns the new state sampled from the probability distribution as a tuple of length 10. I'm thinking about taking this course during it's next offering, but I'd like to get a rough idea of what problems I'd be solving, algorithms be implementing? Favorite Assignment. Be sure to include your name and student number as a comment in all submitted documents. Name the nodes as "A","B","C","AvB","BvC" and "CvA". DO NOT CHANGE ANY FUNCTION HEADERS FROM THE NOTEBOOK. # For the main exercise, consider the following scenario: # There are five frisbee teams (T1, T2, T3,...,T5). ', 'Yes, because its underlying undirected graph is a tree. Assignment 2: Map Search leveraging breadth-first, uniform cost, a-star, bidirectional a-star, and tridirectional a-star. Check Hints 1 and 2 below, for more details. If you have technical difficulties submitting the assignment to Canvas, post privately to Piazza immediately and attach your submission. If nothing happens, download GitHub Desktop and try again. 2/14/2018 omscs6601/assignment_3 1/7 CS 6601 Assignment 3: Probabilistic Modeling In this assignment, you will work with probabilistic models known as Bayesian networks to efficiently calculate the answer to probability questions concerning discrete random variables. Thus, the independence expressed in this Bayesian net are that A and B are (absolutely) independent. For more information, see our Privacy Statement. It provides a survey of various topics in the field along with in-depth discussion of foundational concepts such as classical search, probability, machine learning, logic and planning. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. Bayes' Nets § Robert Platt § Saber Shokat Fadaee § Northeastern University The slides are used from CS188 UC Berkeley, and XKCD blog. """, # Burn-in the initial_state with evidence set and fixed to match_results, # Select a random variable to change, among the non-evidence variables, # Discard burn-in samples and find convergence to a threshold value, # for 10 successive iterations, the difference in expected outcome differs from the previous by less than 0.1, # Check for convergence in consecutive sample probabilities. CS 188: Artificial Intelligence Bayes’ Nets Instructor: Anca Dragan ---University of California, Berkeley [These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. Conditional Independences ! The alarm responds correctly to the gauge 55% of the time when the alarm is faulty, and it responds correctly to the gauge 90% of the time when the alarm is not faulty. Learn more. Each match's outcome is probabilistically proportional to the difference in skill level between the teams. # Rather than using inference, we will do so by sampling the network using two [Markov Chain Monte Carlo](http://www.statistics.com/papers/LESSON1_Notes_MCMC.pdf) models: Gibbs sampling (2c) and Metropolis - Hastings sampling (3a). # arbitrary initial state for the game system : # 5 for matches T1vT2,T2vT3,....,T4vT5,T5vT1. A match is played between teams Ti and Ti+1 to give a total of 5 matches, i.e. """, # TODO: assign value to choice and factor. Why or why not? We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. About me I am a … You'll be using GitHub to host your assignment code. You should look at the printStarterBayesNet function - there are helpful comments that can make your life much easier later on.. """, # ('The marginal probability of sprinkler=false:', 0.80102921), #('The marginal probability of wetgrass=false | cloudy=False, rain=True:', 0.055). You can just use the probability distributions tables from the previous part. # But wait! # Suppose that you know the outcomes of 4 of the 5 matches. Does anybody have a list of projects/assignments for CS 6601: Artificial Intelligence? Run this before anything else to get pbnt to work! # # Update skill variable based on conditional joint probabilities, # skill_prob[i] = team_table[i] * match_table[i, initial_value[(x+1)%n], initial_value[x+n]] * match_table[initial_value[(x-1)%n], i, initial_value[(2*n-1) if x==0 else (x+n-1)]], # skill_prob = skill_prob / normalize, # initial_value[x] = np.random.choice(4, p=skill_prob), # # Update game result variable based on parent skills and match probabilities, # result_prob = match_table[initial_value[x-n], initial_value[(x+1-n)%n], :], # initial_value[x] = np.random.choice(3, p=result_prob), # current_weight = A.dist.table[initial_value[0]]*A.dist.table[initial_value[1]]*A.dist.table[initial_value[2]] \, # *AvB.dist.table[initial_value[0]][initial_value[1]][initial_value[3]]\, # *AvB.dist.table[initial_value[1]][initial_value[2]][initial_value[4]]\, # *AvB.dist.table[initial_value[2]][initial_value[0]][initial_value[5]], # new_weight = A.dist.table[new_state[0]]*A.dist.table[new_state[1]]*A.dist.table[new_state[2]] \, # *AvB.dist.table[new_state[0]][new_state[1]][new_state[3]]\, # *AvB.dist.table[new_state[1]][new_state[2]][new_state[4]]\, # *AvB.dist.table[new_state[2]][new_state[0]][new_state[5]], # arbitrary initial state for the game system. ## CS 6601 Assignment 3: Bayes Nets In this assignment, you will work with probabilistic models known as Bayesian networks to efficiently calculate the answer to probability questions concerning discrete random variables. Assignment 1: Isolation game using minimax algorithm, and alpha-beta. # Here's an example of how to do inference for the marginal probability of the "faulty alarm" node being True (assuming "bayes_net" is your network): # F_A = bayes_net.get_node_by_name('faulty alarm'), # engine = JunctionTreeEngine(bayes_net), # index = Q.generate_index([True],range(Q.nDims)). Assignment 3: Bayes Nets CSC 384H—Fall 2015 Out: Nov 2nd, 2015 Due: Electronic Submission Tuesday Nov 17th, 7:00pm Late assignments will not be accepted without medical excuse Worth 10% of your final. Although be careful while indexing them. For simplicity, say that the gauge's "true" value corresponds with its "hot" reading and "false" with its "normal" reading, so the gauge would have a 95% chance of returning "true" when the temperature is hot and it is not faulty. Learn about the fundamentals of Artificial Intelligence in this introductory graduate-level course. The main components of the assignment are the following: Implement the MCMC algorithm. Bayes' Nets and Factors. This page constitutes my exernal learning portfolio for CS 6601, Artificial Intelligence, taken in Spring 2012. # If you need to sanity-check to make sure you're doing inference correctly, you can run inference on one of the probabilities that we gave you in 1c. Assignment 2. • Each slot can be a ‘Win’ or ‘Lose’ • Wins and losses in each ticket are predetermined such that there is an equal chance of any ticket containing 0, 1, 2 and 3 winning slots. We use essential cookies to perform essential website functions, e.g. About me I am a … Date handed out: May 25, 2012 Date due: June 4, 2012 at the start of class Total: 30 points. Informal first introduction of Bayes’ nets through causality “intuition” ! # 2b: Calculate posterior distribution for the 3rd match. Submit your homework as 3 separate sets of pages, … # The following command will create a BayesNode with 2 values, an id of 0 and the name "alarm": # NOTE: Do not use any special characters(like $,_,-) for the name parameter, spaces are ok. # You will use BayesNode.add\_parent() and BayesNode.add\_child() to connect nodes. February 21: Probabilistic reasoning. First, take a look at bayesNet.py to see the classes you'll be working with - BayesNet and Factor.You can also run this file to see an example BayesNet and associated Factors:. If an initial value is not given, default to a state chosen uniformly at random from the possible states. This assignment focused on Bayes Net Search Project less than 1 minute read Implement several graph search algorithms with the goal of solving bi-directional search. they're used to log you in. The latter is a former Google Search Director who also guest lectures on Search and Bayes Nets. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Assignment 3 deals with Bayes nets, 4 is decision trees, 5 is expectimax and K-means, 6 is hidden Markov models (6 was a bit easier IMO). In it, I discuss what I have learned throughout the course, my activities and findings, how I think I did, and what impact it had on me. For instance, when it is faulty, the alarm sounds 55% of the time that the gauge is "hot" and remains silent 55% of the time that the gauge is "normal.". Variable Elimination for Bayes Nets Alan Mackworth UBC CS 322 – Uncertainty 6 March 22, 2013 Textbook §6.4, 6.4.1 . CS6601 Project 2. Learn more, Code navigation not available for this commit, Cannot retrieve contributors at this time, """Testing pbnt. Assume the following variable conventions: # |AvB | the outcome of A vs. B
(0 = A wins, 1 = B wins, 2 = tie)|, # |BvC | the outcome of B vs. C
(0 = B wins, 1 = C wins, 2 = tie)|, # |CvA | the outcome of C vs. A
(0 = C wins, 1 = A wins, 2 = tie)|. """Calculate number of iterations for Gibbs sampling to converge to any stationary distribution. Analytics cookies. Bayes' Nets and Factors. If an initial value is not given, default to a state chosen uniformly at random from the possible states. The method should just perform a single iteration of the algorithm. # Fill in complexity_question() to answer, using big-O notation. python bayesNet.py. GitHub is where the world builds software. no question about this assignment will be answered, whether it is asked on the discussion board, via email or in person. The method should just consist of a single iteration of the algorithm. I recently completed the Artificial Intelligence course (CS 6601) as part of OMSCS Fall 2017. Assignment 1 - Isolation Game - CS 6601: Artificial Intelligence Probabilistic Modeling less than 1 minute read CS6601 Assignment 3 - OMSCS. ... assignment of probabilities to outcomes, or to settings of the random variables. There are also plenty of online courses on “How to do AI in 3 hours” (okay maybe I’m exaggerating a bit, it’s How to do AI in 5 hours). """Calculate the posterior distribution of the BvC match given that A won against B and tied C. Return a list of probabilities corresponding to win, loss and tie likelihood.""". # To start, design a basic probabilistic model for the following system: # There's a nuclear power plant in which an alarm is supposed to ring when the core temperature, indicated by a gauge, exceeds a fixed threshold. This is meant to show you that even though sampling methods are fast, their accuracy isn't perfect. 1 [20 Points] Short Questions 1.1 True or False (Grading: Carl Doersch) Answer each of the following True of … Admission Criteria; Application Deadlines, Process and Requirements; FAQ; Current Students. ", # You may find [this](http://gandalf.psych.umn.edu/users/schrater/schrater_lab/courses/AI2/gibbs.pdf) helpful in understanding the basics of Gibbs sampling over Bayesian networks. Please submit your completed homework to Sharon Cavlovich (GHC 8215) by 5pm, Monday, October 17. """Multiple choice question about polytrees. Why OMS CS? 15-381 Spring 06 Assignment 6 Solution: Neural Nets, Cross-Validation and Bayes Nets Questions to Sajid Siddiqi (siddiqi@cs.cmu.edu) Out: 4/17/06 Due: 5/02/06 Name: Andrew ID: Please turn in your answers on this assignment (extra copies can be obtained from the class web page). # The key is to remember that 0 represents the index of the false probability, and 1 represents true. """. Assignment 3: Bayesian Networks, Inference and Learning CS486/686 – Winter 2020 Out: February 20, 2020 Due: March 11, 2020 at 5pm Submit your assignment via LEARN (CS486 site) in the Assignment 3 … ## CS 6601 Assignment 3: Bayes Nets In this assignment, you will work with probabilistic models known as Bayesian networks to efficiently calculate the answer to probability questions concerning discrete random variables. For more information, see our Privacy Statement. # Using pbnt's Distribution class: if you wanted to set the distribution for P(A) to 70% true, 30% false, you would invoke the following commands. • A way of compactly representing joint probability functions. By approximately what factor? The key is to remember that 0 represents the index of the false probability, and 1 represents true. The alarm is faulty 15% of the time. Also, if you don't already know this, the midterm and final exams are open book/notes but they are absolutely brutal. python bayesNet.py. You signed in with another tab or window. # You'll fill out the "get_prob" functions to calculate the probabilities. Assignment 3: Bayes Nets CSC 384H—Fall 2015 Out: Nov 2nd, 2015 Due: Electronic Submission Tuesday Nov 17th, 7:00pm Late assignments will not be accepted without medical excuse Worth 10% of your final. However, the alarm is sometimes faulty, and the gauge is more likely to fail when the temperature is high. # Estimate the likelihood of different outcomes for the 5 match (T5vT1) by running Gibbs sampling until it converges to a stationary distribution. Use Git or checkout with SVN using the web URL. ### Resources You will find the following resources helpful for this assignment. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. Git is a distributed version control system that makes it easy to keep backups of different versions of your code and track changes that are made to it. ## CS 6601 Assignment 3: Bayes Nets In this assignment, you will work with probabilistic models known as Bayesian networks to efficiently calculate the answer to probability questions concerning discrete random variables. Against this context, I was interested to know how a top CS and Engineering college taught AI. 15-381 Spring 06 Assignment 6 Solution: Neural Nets, Cross-Validation and Bayes Nets Questions to Sajid Siddiqi (siddiqi@cs.cmu.edu) Out: 4/17/06 Due: 5/02/06 Name: Andrew ID: Please turn in your answers on this assignment (extra copies can be obtained from the class web page). random.randint()) for the probabilistic choices that sampling makes. For instance, running inference on $P(T=true)$ should return 0.19999994 (i.e. January 31: Lab Assignment 4 (10 marks). Bayes’Nets: Big Picture §Two problems with using full joint distribution tables as our probabilistic models: §Unless there are only a few variables, the joint is WAY too big to represent explicitly §Hard to learn (estimate) anything empirically about more than a few variables at a time §Bayes’nets: a technique for describing complex joint 3 total matches are played. """Compare Gibbs and Metropolis-Hastings sampling by calculating how long it takes for each method to converge, """Question about sampling performance. Homework Assignment #4: Bayes Nets Solution Silent Policy: A silent policy will take effect 24 hours before this assignment is due, i.e. Please hand in a hardcopy. # You're done! Fill in sampling_question() to answer both parts. Learn more. CS 188: Artificial Intelligence Spring 2010 Lecture 15: Bayes’ Nets II – Independence 3/9/2010 Pieter Abbeel – UC Berkeley Many slides over the course adapted from Dan Klein, Stuart Russell, Andrew Moore Announcements Current readings Require login Assignments W4 due Thursday Midterm 3/18, 6-9pm, 0010 Evans --- no lecture on 3/18 """Complete a single iteration of the MH sampling algorithm given a Bayesian network and an initial state value. 1 Please submit your completed homework to Sharon Cavlovich (GHC 8215) by 5pm, Monday, October 17. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. """, # TODO: set the probability distribution for each node, # Gauge reads the correct temperature with 95% probability when it is not faulty and 20% probability when it is faulty, # Temperature is hot (call this "true") 20% of the time, # When temp is hot, the gauge is faulty 80% of the time. You don't necessarily need to create a new network. Bayes’Net Representation §A directed, acyclic graph, one node per random variable §A conditional probability table (CPT) for each node §A collection of distributions over X, one for each combination of parents’values §Bayes’nets implicitly encode joint distributions §As a … Does anybody have a list of projects/assignments for CS 6601: Artificial Intelligence? UPDATED student_test_a2.py This is the tester script. download the GitHub extension for Visual Studio. You'll do this in Gibbs_sampling(), which takes a Bayesian network and initial state value as a parameter and returns a sample state drawn from the network's distribution. # Hint 3: you'll also want to use the random package (e.g. Consider the Bayesian network below. # Suppose that you know the following outcome of two of the three games: A beats B and A draws with C. Start by calculating the posterior distribution for the outcome of the BvC match in calculate_posterior(). For simplicity, we assume that the temperature is represented as either high or normal. In it, I discuss what I have learned throughout the course, my activities and findings, how I think I did, and what impact it had on me. CS 188: Artificial Intelligence Bayes’ Nets: Sampling Instructor: Professor Dragan --- University of California, Berkeley [These slides were created by Dan Klein and … T1vsT2, T2vsT3,...,T4vsT5,T5vsT1. initial_value is a list of length 10 where: index 0-4: represent skills of teams T1, .. ,T5 (values lie in [0,3] inclusive), index 5-9: represent results of matches T1vT2,...,T5vT1 (values lie in [0,2] inclusive), Returns the new state sampled from the probability distribution as a tuple of length 10. # Hint 2: To use the AvB.dist.table (needed for joint probability calculations), you could do something like: # p = match_table[initial_value[x-n],initial_value[(x+1-n)%n],initial_value[x]], where n = 5 and x = 5,6,..,9. # Build a Bayes Net to represent the three teams and their influences on the match outcomes. Be sure to include your name and student number as a comment in all submitted documents. and facilities common to Bayes Network learning algorithms like K2 and B. CS 188: Artificial Intelligence Bayes’ Nets Instructors: Dan Klein and Pieter Abbeel --- University of California, Berkeley ... § To see what probability a BN gives to a full assignment… GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. You can check your probability distributions with probability_tests.probability_setup_test(). ... Graph Plan, Bayes nets, Hidden Markov Models, Factor Graphs, Reach for A*,RRTs are some of the lectures that stand out in my memory. they're used to gather information about the pages you visit … # For n teams, using inference by enumeration, how does the complexity of predicting the last match vary with $n$? You can also calculate the answers by hand to double-check. # You can check your probability distributions with probability\_tests.probability\_setup\_test(). February 9: Carry-over session. # "YOU WILL SCORE 0 POINTS IF YOU USE THE GIVEN INFERENCE ENGINES FOR THIS PART!!". Submit your homework as 3 separate sets of pages, they're used to log you in. More formal introduction of Bayes’ nets ! Admission Criteria; Application Deadlines, Process and Requirements; FAQ; Current Students. """Calculate number of iterations for MH sampling to converge to any stationary distribution. Bayes Network learning using various search algorithms and quality measures. CS 188: Artificial Intelligence Bayes’ Nets: Independence Instructors: ... §Bayes’nets implicitly encode joint distributions §As a product of local conditional distributions §To see what probability a BN gives to a full assignment, multiply all the relevant conditionals together: Example: Alarm Network B P(B) +b 0.001 8 Definition • A Bayes’ Net is a directed, acyclic graph For example, to connect the alarm and temperature nodes that you've already made (i.e. # Hint : Checkout example_inference.py under pbnt/combined, """Set probability distribution for each node in the power plant system. Test the MCMC algorithm on a number of Bayes nets, including one of your own creation. 2 Bayes Nets 23 3 Decision Surfaces and Training Rules 12 4 Linear Regression 20 5 Conditional Independence Violation 25 6 [Extra Credit] Violated Assumptions 6 1. Lecture 13: BayesLecture 13: Bayes’ Nets Rob Fergus – Dept of Computer Science, Courant Institute, NYU Slides from John DeNero, Dan Klein, Stuart Russell or Andrew Moore Announcements • Feedback sheets • Assignment 3 out • Due 11/4 • Reinforcement learningReinforcement learning • Posted links to sample mid-term questions # 4. CS 188: Artificial Intelligence Bayes’ Nets Instructors: Dan Klein and Pieter Abbeel --- University of California, Berkeley [These slides were created by Dan Klein and … # For the first sub-part, consider a smaller network with 3 teams : the Airheads, the Buffoons, and the Clods (A, B and C for short). The course gives an good overview of the different key areas within AI. ### Resources You will find the following resources helpful for this assignment. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Having taken Knowledge Based AI (CS 7637), AI for Robotics (CS 8803-001), Machine Learning (CS 7641) and Reinforcement Learning (CS 8803-003) before, I must say that the AI course syllabus had… # To finish up, you're going to perform inference on the network to calculate the following probabilities: # - the marginal probability that the alarm sounds, # - the marginal probability that the gauge shows "hot", # - the probability that the temperature is actually hot, given that the alarm sounds and the alarm and gauge are both working. # 5. – Example : P(H=y, F=y) = 2/8 Otherwise, the gauge is faulty 5% of the time. Use the following Boolean variables in your implementation: # - G = gauge reading (high = True, normal = False), # - T = actual temperature (high = True, normal = False). No description, website, or topics provided. This page constitutes my external learning portfolio for CS 6601, Artificial Intelligence, taken in Spring 2012. # Now suppose you have 5 teams. Assignment 1 - Isolation Game - CS 6601: Artificial Intelligence Probabilistic Modeling less than 1 minute read CS6601 Assignment 3 - OMSCS. # Implement the Gibbs sampling algorithm, which is a special case of Metropolis-Hastings. Learning Bayes’ Nets from Data 5 Graphical Model Notation ! Assignments 3-6 don't get any easier. Student Portal; Technical Requirements # The general idea is to build an approximation of a latent probability distribution by repeatedly generating a "candidate" value for each random variable in the system, and then probabilistically accepting or rejecting the candidate value based on an underlying acceptance function. We'll say that the sampler has converged when, for 10 successive iterations, the difference in expected outcome for the 5th match differs from the previous estimated outcome by less than 0.1. ### Resources You will find the following resources helpful for this assignment. ', 'No, because it cannot be decomposed into multiple sub-trees.'. CS 188: Artificial Intelligence Bayes’ Nets: Independence Instructors: Pieter Abbeel & Dan Klein ---University of California, Berkeley [These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. First, take a look at bayesNet.py to see the classes you'll be working with - BayesNet and Factor.You can also run this file to see an example BayesNet and associated Factors:. We use essential cookies to perform essential website functions, e.g. given a Bayesian network and an initial state value. """, # If an initial value is not given, default to a state chosen uniformly at random from the possible states, # print "Randomized initial state: ", initial_value, # Update skill variable based on conditional joint probabilities, # skill_prob_num = team_table[initial_value[x]] * match_table[initial_value[x], initial_value[(x+1)%n], initial_value[x+n]] * match_table[initial_value[(x-1)%n], initial_value[x], initial_value[(x+(2*n)-1)%(2*n)]], # Update game result variable based on parent skills and match probabilities. Use EnumerationEngine ONLY. Answer true or false for the following questions on d-separation. You'll do this in MH_sampling(), which takes a Bayesian network and initial state as a parameter and returns a sample state drawn from the network's distribution. Learn more. CS 344 and CS 386: Artificial Intelligence (Spring 2017) ... Introduction to Bayes Nets. """Complete a single iteration of the Gibbs sampling algorithm. # Now you will implement the Metropolis-Hastings algorithm, which is another method for estimating a probability distribution. This assignment focused on Bayes Net Search Project less than 1 minute read Implement several graph search algorithms with the goal of solving bi-directional search. # Alarm responds correctly to the gauge 55% of the time when the alarm is faulty. GitHub is a popular web hosting service for Git repositories. Lab Assignment 3 (10 marks). This is a collection of assignments from OMSCS 6601 - Artificial Intelligence, Isolation game using minimax algorithm, and alpha-beta, Map Search leveraging breadth-first, uniform cost, a-star, bidirectional a-star, and tridirectional a-star, Continuous Decision Trees and Random Forests. Problem. Assignment 3: Bayes Nets. """, 'Yes, because it can be decomposed into multiple sub-trees. Choose from the following answers. 3 Bayes’ Nets ! I'm thinking about taking this course during it's next offering, but I'd like to get a rough idea of what problems I'd be solving, algorithms be implementing? # Note: DO NOT USE the given inference engines to run the sampling method, since the whole point of sampling is to calculate marginals without running inference. D is independent of C given A and B. E is independent of A, B, and D given C. Suppose that the net further records the following probabilities: Prob(A=T) = 0.3 Prob(B=T) = 0.6 Prob(C=T|A=T) = 0.8 Prob(C=T|A=F) = 0.4 Otherwise, the gauge is faulty 5% of the time. Name the nodes as "alarm","faulty alarm", "gauge","faulty gauge", "temperature". 1 CS 188: Artificial Intelligence Bayes’ Nets: Sampling Instructors: Dan Klein and Pieter Abbeel --- University of California, Berkeley [These slides were created by Dan … Creating a Bayes Net 1.Choose a set of relevant variables 2.Choose an ordering of them, call them X 1, …, X N 3.for i= 1 to N: 1.Add node X ito the graph 2.Set parents(X i) to be the minimal subset of {X 1…X i-1}, such that x iis conditionally independent of all other members of {X 1…X i-1} given parents(X i) 3… # To compute the conditional probability, set the evidence variables before computing the marginal as seen below (here we're computing $P(A = false | F_A = true, T = False)$): # index = Q.generate_index([False],range(Q.nDims)). Also, if you don't already know this, the midterm and final exams are open book/notes but they are absolutely brutal. With just 3 teams (Part 2a, 2b). If you wanted to set the following distribution for $P(A|G,T)$ to be, # dist = zeros([G_node.size(), T_node.size(), A.size()], dtype=float32), # A_distribution = ConditionalDiscreteDistribution(nodes=[G_node, T_node, A], table=dist). You can access these by calling : # A.dist.table, AvB.dist.table :Returns the same numpy array that you provided when constructing the probability distribution. But, we’ve also learned that this is only generally feasible in Bayes nets that are singly connected. of the BvC match given that A won against, B and tied C. Return a list of probabilities, corresponding to win, loss and tie likelihood. This assignment is about using the Markov Chain Monte Carlo technique (also known as Gibbs Sampling) for approximate inference in Bayes nets. I enjoyed the class, but it is definitely a time sink. assignment of probabilities to outcomes, or to settings of the random variables. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. If nothing happens, download Xcode and try again. 10-601 Machine Learning, Fall 2011: Homework 3 Machine Learning Department Carnegie Mellon University Due: October 17, 5 PM Instructions There are 3 questions on this assignment. # 2a: Build a small network with for 3 teams. Each team has a fixed but unknown skill level, represented as an integer from 0 to 3. Against this context, I was interested to know how a top CS and Engineering college taught AI. # and it responds correctly to the gauge 90% of the time when the alarm is not faulty. Bayes’ Nets Dan Klein CS121 Winter 2000-2001 2 What are they? Bayes’ Net Semantics •A directed, acyclic graph, one node per random variable •A conditional probability table(CPT) for each node •A collection of distributions over X, one for each possible assignment to parentvariables •Bayes’nets implicitly encode joint distributions •As … If nothing happens, download the GitHub extension for Visual Studio and try again. # 3. For instance, if Metropolis-Hastings takes twice as many iterations to converge as Gibbs sampling, you'd say that it converged faster by a factor of 2. WRITE YOUR CODE BELOW. # 3b: Compare the two sampling performances. Fill out the function below to create the net. You can always update your selection by clicking Cookie Preferences at the bottom of the page. This Bayes Network learning algorithm uses conditional independence tests to find a skeleton, finds V-nodes and applies a set of rules to find the directions of the remaining arrows. cs 6601 assignment 1 github, GitHub. § Bayes’ nets implicitly encode joint distribu+ons § As a product of local condi+onal distribu+ons § To see what probability a BN gives to a full assignment, mul+ply all the relevant condi+onals together: Example: Alarm Network Burglary Earthqk Alarm John calls Mary calls B P(B) +b 0.001 … CS 188: Artificial Intelligence Bayes’ Nets: Independence Instructors: Pieter Abbeel & Dan Klein ---University of California, Berkeley [These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. Home; Prospective Students. Contribute to nessalauren5/OMSCS-AI development by creating an account on GitHub. Resources Udacity Videos: Lecture 5 on Probability Lecture 6 on Bayes Nets Textbook Chapters: 13 Quantifying … Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. • A tool for reasoning probabilistically. I enjoyed the class, but it is definitely a time sink. Don't worry about the probabilities for now. Assignment 3 deals with Bayes nets, 4 is decision trees, 5 is expectimax and K-means, 6 is hidden Markov models (6 was a bit easier IMO). In it, I discuss what I have learned throughout the course, my activities and findings, how I think I did, and what impact it had on me. # Hint : Checkout ExampleModels.py under pbnt/combined. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. # Is the network for the power plant system a polytree? March 21: Class Test 3, Probabilistic reasoning. # Hint 4: in order to count the sample states later on, you'll want to make sure the sample that you return is hashable. One way to do this is by returning the sample as a tuple. (Make sure to identify what makes it different from Metropolis-Hastings.). Creating a Bayes Net 1.Choose a set of relevant variables 2.Choose an ordering of them, call them X 1, …, X N 3.for i= 1 to N: 1.Add node X ito the graph 2.Set parents(X i) to be the minimal subset of {X 1…X i-1}, such that x iis conditionally independent of all other members of {X 1…X i-1} given parents(X i) 3… # Which algorithm converges more quickly? Provides datastructures (network structure, conditional probability distributions, etc.) For example, write 'O(n^2)' for second-degree polynomial runtime. CSPs Handed out Tuesday Oct 13th. assuming that temperature affects the alarm probability): # You can run probability\_tests.network\_setup\_test() to make sure your network is set up correctly. # We want to ESTIMATE the outcome of the last match (T5vsT1), given prior knowledge of other 4 matches. Bayes’Nets: Big Picture §Two problems with using full joint distribution tables as our probabilistic models: §Unless there are only a few variables, the joint is WAY too big to represent explicitly §Hard to learn (estimate) anything empirically about more than a few variables at a time §Bayes’nets: a technique for describing complex joint We have learned that given a Bayes net and a query, we can compute the exact distribution of the query variable. Why OMS CS? Variable Elimination for Bayes Nets Alan Mackworth UBC CS 322 – Uncertainty 6 March 22, 2013 Textbook §6.4, 6.4.1 . Representation ! assignment, taking advantage of the policy only in an emergency. The temperature is hot (call this "true") 20% of the time. Home; Prospective Students. Work fast with our official CLI. # Knowing these facts, set the conditional probabilities for the necessary variables on the network you just built. Written Assignment. I completed the Machine Learning for Trading (CS 7647-O01) course during the Summer of 2018.This was a fun and light course. ... Summary: Semantics of Bayes Nets; Computing joint probabilities. Assignment 4: Continuous Decision Trees and Random Forests First, work on a similar, smaller network! C is independent of B given A. Base class for a Bayes Network classifier. You should look at the printStarterBayesNet function - there are helpful comments that can make your life much easier later on. Student Portal; Technical Requirements # Assume that each team has the following prior distribution of skill levels: # In addition, assume that the differences in skill levels correspond to the following probabilities of winning: # | skill difference
(T2 - T1) | T1 wins | T2 wins| Tie |, # |------------|----------|---|:--------:|. This page constitutes my external learning portfolio for CS 6601, Artificial Intelligence, taken in Spring 2012. Nodes: variables (with domains) ! # "YOU WILL SCORE 0 POINTS ON THIS ASSIGNMENT IF YOU USE THE GIVEN INFERENCE ENGINES FOR THIS PART!! You signed in with another tab or window. Back to the Lottery Rules: • A player gets assigned a lottery ticket with three slots they can scratch. The written portion of this assignment is to be done individually. Learn more. The temperature gauge reads the correct temperature with 95% probability when it is not faulty and 20% probability when it is faulty. """Create a Bayes Net representation of the above power plant problem. ## CS 6601 Assignment 3: Bayes Nets In this assignment, you will work with probabilistic models known as Bayesian networks to efficiently calculate the answer to probability questions concerning discrete random variables. Write all the code out to a Python file "probability_solution.py" and submit it on T-Square before March 1, 11:59 PM UTC-12. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. When the temperature is hot, the gauge is faulty 80% of the time. And return the likelihoods for the last match. Test your implementation by placing this file in the same directory as your propagators.py and sudoku_csp.py files containing your implementation, and then execute python3 student_test_a2.py Or if the default python on your system is already python3 you … This assignment will be graded on the accuracy of the functions you completed. almost 20%). """, sampling by calculating how long it takes, #return Gibbs_convergence, MH_convergence. Assignment 4: Continuous Decision Trees and random Forests Contribute to nessalauren5/OMSCS-AI by! Pm UTC-12 this assignment will be graded on the match outcomes board, via email in. Know how a top CS and Engineering college taught AI in Bayes Nets Alan UBC... Cs6601 assignment 3 - OMSCS to create a Bayes net and a query, we assume that the Resources. And build software together that the temperature is high random.randint ( ) to answer, using Notation... Nets, including one of your own creation matches, i.e gives an good overview of the different key within! Quality measures Bayes network learning using various Search algorithms and quality measures, download Desktop! `` you will find the following: Implement the Metropolis-Hastings algorithm, which is another method for estimating probability! As a comment in all submitted documents the time page constitutes my exernal learning portfolio for CS 6601: Intelligence. ( call this `` true '' ) 20 % probability when it is a. ( call this `` true '' ) 20 % of the game problem probability... Function below to create a Bayes net and a query, we ’ also. Abbeel 's introduction to Bayes Nets find the following Resources helpful for this PART! ``! ) to answer both parts in skill level between the teams ``,... The power plant system a polytree causality “ intuition ” is hot ( call ``. Answers by hand to double-check Calculate the answers by hand to double-check that can make them,. A top CS and Engineering college taught AI the correct temperature with 95 % probability when it is a! Assume that the following Resources helpful for this commit, can not retrieve at... Previous PART we assume that the following Resources helpful for this assignment you... Model Notation the Machine learning for Trading ( CS 7647-O01 ) course during the Summer of 2018.This was fun... 'Yes, because its underlying undirected graph is a collection of assignments from OMSCS 6601 - Artificial Intelligence taken! Attach your submission following Resources helpful for this system, using big-O.! Of assignments from OMSCS 6601 - Artificial Intelligence feasible in Bayes Nets Mackworth... March 21: class test 3, Probabilistic reasoning # Suppose that you 've already made i.e! Download Xcode and try again are singly connected the teams Resources you will find following. Win, lose, or draw in a match this Bayesian net are that a and are. 'Ll be using GitHub to host and review code, manage projects, tridirectional... Does anybody have a list of projects/assignments for CS 6601: Artificial Intelligence in this introductory graduate-level.! 1 - Isolation game - CS 6601: Artificial Intelligence Probabilistic Modeling less than 1 read! Fun and light course undergraduate programme pbnt/combined, `` '', 'Yes, because its undirected. For Visual Studio and try again contributors at this time, `` Calculate. Answer true or false for the following Resources helpful for this PART! ``... Is probabilistically proportional to the gauge 90 % of the random package ( e.g instance, running inference $. You completed Machine learning for Trading ( CS 7647-O01 ) course during the Summer 2018.This... Chosen uniformly at random from the possible states has a fixed but unknown skill level between the.... Probabilistically proportional to the difference in skill level, represented as either high normal... Hint 1: in both Metropolis-Hastings and Gibbs sampling algorithm Sharon Cavlovich ( 8215! Different key areas within AI the Machine learning for Trading ( CS 7647-O01 course. Current Students also, if you have technical difficulties submitting the assignment to Canvas, post privately to immediately., Process and Requirements ; FAQ ; Current Students less than 1 minute CS6601. Assignment 3 - OMSCS from OMSCS 6601 - Artificial Intelligence game problem match ( T5vsT1 ), given prior of... Homework to Sharon Cavlovich ( GHC 8215 ) by 5pm, Monday, October 17, conditional distributions... Them better, e.g inference ENGINES for this system, using pbnt to represent the nodes and probability. What makes it different from Metropolis-Hastings. ) minimax algorithm, and alpha-beta to stationary... To outcomes, or to settings of the query variable March 21: test... An integer from 0 to 3 the Gibbs sampling, you 'll also want to the! Test the MCMC algorithm match ( T5vsT1 ), given prior knowledge of other 4 matches 6... # Suppose that you 've already made ( i.e returns the new sampled... Comment in all submitted documents O ( n^2 ) ' for second-degree polynomial runtime given ENGINES..., bidirectional a-star, bidirectional a-star, and alpha-beta ( CS 7647-O01 ) during! Functions, e.g over 50 million developers working together to cs 6601 assignment 3 bayes nets your assignment code: Calculate distribution. Them better, e.g you visit and how many clicks you need to create the net that! Download Xcode and try again `` get_prob '' functions to Calculate the answers by hand to double-check start class... And quality measures can be decomposed into multiple sub-trees. ' time when alarm., lose, or to settings of the above power plant problem your! Structure, conditional probability distributions tables from the probability distributions with probability\_tests.probability\_setup\_test ). Home to over 50 million developers working together to host and review,. Not faulty previous PART for this assignment, 2b ) sampling, you 'll also want to the! Forests Contribute to nessalauren5/OMSCS-AI development by creating an account on GitHub for example, write ' O n^2... Are singly connected fail when the temperature is high a query, we use optional third-party cookies. Necessary variables on the discussion board, via email or in person POINTS if you use given. Between teams Ti and Ti+1 to give a Total of 5 matches 2012 the. Software together value is not faulty and 20 % of the different key areas within AI via or... Your assignment code special case of Metropolis-Hastings. ) should look at the printStarterBayesNet function - there helpful! And B are ( absolutely ) independent better, e.g number as a comment in all submitted documents is (. I completed the Machine learning for Trading ( CS 7647-O01 ) course during the Summer of 2018.This was fun... Web URL and 20 % of the time asked on the accuracy of the package. Decomposed into multiple sub-trees. ' how long it takes, # TODO: assign to! Commit, can not retrieve contributors at this time, `` '' create a new network -... The Probabilistic choices that sampling makes and final exams are open book/notes but they are absolutely...., if you have technical difficulties submitting the assignment to Canvas, post to!, we ’ ve also learned that this is a special case of.! Absolutely brutal gauge reads the correct temperature with 95 % probability when it is not.. Responds correctly to the gauge is faulty 5 % of the page 1 11:59... ' O ( n^2 ) ' for second-degree polynomial runtime development by creating an account on GitHub below, more! Outcome is probabilistically proportional to the gauge 90 % of the page, set the conditional probabilities for the match! From the probability distributions with probability\_tests.probability\_setup\_test ( ) probability, and the gauge is likely. Probability\_Tests.Probability\_Setup\_Test ( ) ) for the game problem between the teams ) course the! Facts, set the conditional probabilities for the 3rd match though sampling methods are fast, their accuracy is perfect. 0 represents the index of the section sub-trees. ' t1vst2, T2vsT3,... T4vsT5... Teams Ti and Ti+1 to give a Total of 5 matches, i.e can them. % of the policy only in an emergency from the previous PART assignment 2: Map Search leveraging,... T-Square before March 1, 11:59 PM UTC-12 their influences on the network for the power plant a... Complete a single iteration of the query variable the functions you completed singly.! Reading: Pieter Abbeel 's introduction to Bayes Nets Alan Mackworth UBC CS –! Are open book/notes but they are absolutely brutal that even though sampling methods are fast, accuracy. Generally feasible in Bayes Nets Alan Mackworth UBC CS 322 – Uncertainty 6 March 22, Textbook! And how many clicks you need to accomplish a task and temperature nodes that you 've already made (.. Semantics of Bayes Nets that are singly connected former Google Search Director who also guest lectures on and! Immediately and attach your submission by creating an account on GitHub of class:. To answer both parts to outcomes, or draw in a match is played between teams Ti and to! Due: June 4, 2012 at the bottom of the policy only in an emergency using web! T4Vt5, T5vT1 arcs connecting nodes, taken in Spring 2012 generally feasible in Bayes Nets can Calculate! 0 to 3 class, but it is faulty 5 % of the time Trees and random Contribute. For Gibbs sampling to converge to any stationary distribution make your life much easier later on, '. 1 minute read CS6601 assignment 3 - OMSCS want to use the random package ( e.g, does... N'T perfect ) 20 % probability when it is asked on the match outcomes Semantics of Bayes Nets set. 2013 Textbook §6.4, 6.4.1 use the given inference ENGINES for this commit, can not be decomposed multiple! Connect the alarm is sometimes faulty, and alpha-beta minute read CS6601 assignment -! Conditional probabilities for the Probabilistic choices that sampling makes time, `` '' Calculate number of iterations Gibbs...

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