[Coursera] Probabilistic Graphical Models
Stanford University



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Coursera - Probabilistic Graphical Models (547 files)
Assignments/Assignment 1/Assignment 1.pdf282.65kB
Assignments/Assignment 1/AssignmentToIndex.m0.60kB
Assignments/Assignment 1/ComputeJointDistribution.m1.13kB
Assignments/Assignment 1/ComputeMarginal.m1.27kB
Assignments/Assignment 1/ConvertNetwork.m3.85kB
Assignments/Assignment 1/Credit_net.net4.57kB
Assignments/Assignment 1/FactorMarginalization.m1.59kB
Assignments/Assignment 1/FactorProduct.m2.33kB
Assignments/Assignment 1/FactorTutorial.m6.48kB
Assignments/Assignment 1/GetValueOfAssignment.m0.81kB
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Assignments/Assignment 1/StandardizeFactors.m0.58kB
Assignments/Assignment 1/submit.m22.94kB
Assignments/Assignment 1/submitWeb.m0.83kB
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Assignments/Assignment 2/AssignmentToIndex.m0.60kB
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Assignments/Assignment 2/PA2Appendix.pdf100.47kB
Assignments/Assignment 2/PA2Description.pdf1.34MB
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Assignments/Assignment 2/sampleFactorList.mat0.40kB
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Assignments/Assignment 2/sendToSamiam.m7.49kB
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Assignments/Assignment 2/sendToSamiamInfo.m0.89kB
Assignments/Assignment 2/sendToSamiamInfoDecoupled.m1.13kB
Assignments/Assignment 2/spinalMuscularAtrophyBayesNet.net4.23kB
Assignments/Assignment 2/submit.m26.86kB
Assignments/Assignment 2/submitWeb.m0.52kB
Assignments/Assignment 3/AssignmentToIndex.m0.65kB
Assignments/Assignment 3/BuildOCRNetwork.m3.35kB
Assignments/Assignment 3/ChooseTopSimilarityFactors.m0.83kB
Assignments/Assignment 3/ComputeAllSimilarityFactors.m0.67kB
Assignments/Assignment 3/ComputeEqualPairwiseFactors.m0.65kB
Assignments/Assignment 3/ComputeImageFactor.m0.70kB
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Assignments/Assignment 3/ComputeSingletonFactors.m0.84kB
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Assignments/Assignment 3/ComputeWordPredictions.m1.18kB
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Assignments/Assignment 3/ImageSimilarity.m0.71kB
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Assignments/Assignment 3/PA3Data.mat14.77kB
Assignments/Assignment 3/PA3Description.pdf423.20kB
Assignments/Assignment 3/PA3Models.mat54.91kB
Assignments/Assignment 3/PA3SampleCases.mat76.08kB
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Assignments/Assignment 3/RunInference.m1.77kB
Assignments/Assignment 3/ScoreModel.m1.17kB
Assignments/Assignment 3/ScorePredictions.m2.24kB
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Assignments/Assignment 3/VisualizeWord.m0.70kB
Assignments/Assignment 3/inference/doinference-linux2.29MB
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Assignments/Assignment 3/inference/doinference.exe3.44MB
Assignments/Assignment 3/inference/inference-src.zip2.23MB
Assignments/Assignment 3/submit.m21.12kB
Assignments/Assignment 3/submitWeb.m0.58kB
Assignments/Assignment 4/Assignment 4.pdf431.40kB
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Assignments/Assignment 4/CliqueTreeCalibrate.m1.85kB
Assignments/Assignment 4/ComputeExactMarginalsBP.m1.03kB
Assignments/Assignment 4/ComputeInitialPotentials.m1.62kB
Assignments/Assignment 4/ComputeJointDistribution.m1.27kB
Assignments/Assignment 4/ComputeMarginal.m1.23kB
Assignments/Assignment 4/CreateCliqueTree.m2.24kB
Assignments/Assignment 4/DecodedMarginalsToChars.m0.22kB
Assignments/Assignment 4/EliminateVar.m1.35kB
Assignments/Assignment 4/FactorMarginalization.m1.69kB
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Assignments/Assignment 4/ObserveEvidence.m2.19kB
Assignments/Assignment 4/PA4Sample.mat220.45kB
Assignments/Assignment 4/PA4Test.mat64.55kB
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Assignments/Assignment 4/StandardizeFactors.m0.62kB
Assignments/Assignment 4/submit.m28.92kB
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Assignments/Assignment 5/Assignment 5.pdf535.01kB
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Assignments/Assignment 5/BlockLogDistribution.m2.88kB
Assignments/Assignment 5/CheckConvergence.m1.16kB
Assignments/Assignment 5/ClusterGraphCalibrate.m3.44kB
Assignments/Assignment 5/ComputeApproxMarginalsBP.m2.33kB
Assignments/Assignment 5/ComputeInitialPotentials.m2.16kB
Assignments/Assignment 5/ConstructRandNetwork.m1.91kB
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Assignments/Assignment 5/CreateClusterGraph.m1.55kB
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Assignments/Assignment 5/GibbsTrans.m1.02kB
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Assignments/Assignment 5/LogProbOfJointAssignment.m0.34kB
Assignments/Assignment 5/MCMCInference.m5.37kB
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Assignments/Assignment 5/MHSWTrans.m3.85kB
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Assignments/Assignment 5/NaiveGetNextClusters.m1.21kB
Assignments/Assignment 5/ObserveEvidence.m2.19kB
Assignments/Assignment 5/SetValueOfAssignment.m0.86kB
Assignments/Assignment 5/SmartGetNextClusters.m1.38kB
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Assignments/Assignment 5/VisualizeMCMCMarginals.m2.55kB
Assignments/Assignment 5/VisualizeToyImageMarginals.m0.37kB
Assignments/Assignment 5/exampleIOPA5.mat44.69kB
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Assignments/Assignment 5/rand.m0.44kB
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Assignments/Assignment 5/smooth.m0.43kB
Assignments/Assignment 5/submit.m37.74kB
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Assignments/Assignment 6/Assignment 6.pdf467.24kB
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Assignments/Assignment 6/CPDFromFactor.m0.78kB
Assignments/Assignment 6/CalculateExpectedUtilityFactor.m0.92kB
Assignments/Assignment 6/EliminateVar.m1.45kB
Assignments/Assignment 6/FactorMarginalization.m1.75kB
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Assignments/Assignment 6/NormalizeCPDFactors.m0.85kB
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Assignments/Assignment 6/ObserveEvidence.m2.32kB
Assignments/Assignment 6/OptimizeLinearExpectations.m1.53kB
Assignments/Assignment 6/OptimizeMEU.m1.29kB
Assignments/Assignment 6/OptimizeWithJointUtility.m1.09kB
Assignments/Assignment 6/PrintFactor.m0.53kB
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Assignments/Assignment 6/TestCases.m4.91kB
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Assignments/Assignment 6/VariableElimination.m1.38kB
Assignments/Assignment 6/submit.m29.00kB
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Assignments/Assignment 7/AssignmentToIndex.m0.61kB
Assignments/Assignment 7/CliqueTreeCalibrate.m5.04kB
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Assignments/Assignment 7/ComputeExactMarginalsBP.m2.26kB
Assignments/Assignment 7/ComputeInitialPotentials.m3.60kB
Assignments/Assignment 7/ComputeJointDistribution.m0.91kB
Assignments/Assignment 7/ComputeMarginal.m0.97kB
Assignments/Assignment 7/ComputeUnconditionedPairFeatures.m0.73kB
Assignments/Assignment 7/ComputeUnconditionedSingletonFeatures.m0.61kB
Assignments/Assignment 7/CreateCliqueTree.m2.12kB
Assignments/Assignment 7/EliminateVar.m1.34kB
Assignments/Assignment 7/EmptyFactorStruct.m0.18kB
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Assignments/Assignment 7/FactorMarginalization.m1.55kB
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Assignments/Assignment 7/FactorProduct.m2.16kB
Assignments/Assignment 7/FactorSum.m2.21kB
Assignments/Assignment 7/GenerateAllFeatures.m2.51kB
Assignments/Assignment 7/GetNextCliques.m1.74kB
Assignments/Assignment 7/GetValueOfAssignment.m0.83kB
Assignments/Assignment 7/IndexToAssignment.m0.59kB
Assignments/Assignment 7/InstanceNegLogLikelihood.m3.05kB
Assignments/Assignment 7/LRAccuracy.m0.66kB
Assignments/Assignment 7/LRCostSGD.m1.50kB
Assignments/Assignment 7/LRPredict.m0.51kB
Assignments/Assignment 7/LRSearchLambdaSGD.m1.10kB
Assignments/Assignment 7/LRTrainSGD.m1.35kB
Assignments/Assignment 7/MaxDecoding.m0.66kB
Assignments/Assignment 7/NumParamsForConditionedFeatures.m0.32kB
Assignments/Assignment 7/NumParamsForUnconditionedFeatures.m0.22kB
Assignments/Assignment 7/ObserveEvidence.m1.91kB
Assignments/Assignment 7/PA7Description.pdf498.81kB
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Type: Course
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Bibtex:
@article{,
title= {[Coursera] Probabilistic Graphical Models},
keywords= {},
journal= {},
author= {Stanford University},
year= {2013},
url= {},
license= {},
abstract= {In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques.




Uncertainty is unavoidable in real-world applications: we can almost never predict with certainty what will happen in the future, and even in the present and the past, many important aspects of the world are not observed with certainty. Probability theory gives us the basic foundation to model our beliefs about the different possible states of the world, and to update these beliefs as new evidence is obtained. These beliefs can be combined with individual preferences to help guide our actions, and even in selecting which observations to make. While probability theory has existed since the 17th century, our ability to use it effectively on large problems involving many inter-related variables is fairly recent, and is due largely to the development of a framework known as Probabilistic Graphical Models (PGMs). This framework, which spans methods such as Bayesian networks and Markov random fields, uses ideas from discrete data structures in computer science to efficiently encode and manipulate probability distributions over high-dimensional spaces, often involving hundreds or even many thousands of variables. These methods have been used in an enormous range of application domains, which include: web search, medical and fault diagnosis, image understanding, reconstruction of biological networks, speech recognition, natural language processing, decoding of messages sent over a noisy communication channel, robot navigation, and many more. The PGM framework provides an essential tool for anyone who wants to learn how to reason coherently from limited and noisy observations.},
superseded= {},
terms= {}
}