[Coursera] Probabilistic Graphical Models
Stanford University

Coursera - Probabilistic Graphical Models (547 files)
Assignments/Assignment 1/Assignment 1.pdf 282.65kB
Assignments/Assignment 1/AssignmentToIndex.m 0.60kB
Assignments/Assignment 1/ComputeJointDistribution.m 1.13kB
Assignments/Assignment 1/ComputeMarginal.m 1.27kB
Assignments/Assignment 1/ConvertNetwork.m 3.85kB
Assignments/Assignment 1/Credit_net.net 4.57kB
Assignments/Assignment 1/FactorMarginalization.m 1.59kB
Assignments/Assignment 1/FactorProduct.m 2.33kB
Assignments/Assignment 1/FactorTutorial.m 6.48kB
Assignments/Assignment 1/GetValueOfAssignment.m 0.81kB
Assignments/Assignment 1/IndexToAssignment.m 0.60kB
Assignments/Assignment 1/ObserveEvidence.m 1.95kB
Assignments/Assignment 1/SetValueOfAssignment.m 1.16kB
Assignments/Assignment 1/StandardizeFactors.m 0.58kB
Assignments/Assignment 1/submit.m 22.94kB
Assignments/Assignment 1/submitWeb.m 0.83kB
Assignments/Assignment 1/submit_input.mat 3.15kB
Assignments/Assignment 2/AssignmentToIndex.m 0.60kB
Assignments/Assignment 2/GetValueOfAssignment.m 0.81kB
Assignments/Assignment 2/IndexToAssignment.m 0.60kB
Assignments/Assignment 2/PA2Appendix.pdf 100.47kB
Assignments/Assignment 2/PA2Description.pdf 1.34MB
Assignments/Assignment 2/SetValueOfAssignment.m 1.15kB
Assignments/Assignment 2/childCopyGivenFreqsFactor.m 0.69kB
Assignments/Assignment 2/childCopyGivenParentalsFactor.m 2.62kB
Assignments/Assignment 2/computeSigmoid.m 0.38kB
Assignments/Assignment 2/constructDecoupledGeneticNetwork.m 3.92kB
Assignments/Assignment 2/constructGeneticNetwork.m 3.08kB
Assignments/Assignment 2/constructSigmoidPhenotypeFactor.m 2.75kB
Assignments/Assignment 2/generateAlleleGenotypeMappers.m 2.03kB
Assignments/Assignment 2/genotypeGivenAlleleFreqsFactor.m 3.10kB
Assignments/Assignment 2/genotypeGivenParentsGenotypesFactor.m 3.26kB
Assignments/Assignment 2/phenotypeGivenCopiesFactor.m 3.30kB
Assignments/Assignment 2/phenotypeGivenGenotypeFactor.m 2.06kB
Assignments/Assignment 2/phenotypeGivenGenotypeMendelianFactor.m 2.32kB
Assignments/Assignment 2/sampleFactorList.mat 0.40kB
Assignments/Assignment 2/sampleFactorListDecoupled.mat 0.44kB
Assignments/Assignment 2/sampleGeneticNetworks.m 4.27kB
Assignments/Assignment 2/sendToSamiam.m 7.49kB
Assignments/Assignment 2/sendToSamiamGeneCopy.m 10.43kB
Assignments/Assignment 2/sendToSamiamInfo.m 0.89kB
Assignments/Assignment 2/sendToSamiamInfoDecoupled.m 1.13kB
Assignments/Assignment 2/spinalMuscularAtrophyBayesNet.net 4.23kB
Assignments/Assignment 2/submit.m 26.86kB
Assignments/Assignment 2/submitWeb.m 0.52kB
Assignments/Assignment 3/AssignmentToIndex.m 0.65kB
Assignments/Assignment 3/BuildOCRNetwork.m 3.35kB
Assignments/Assignment 3/ChooseTopSimilarityFactors.m 0.83kB
Assignments/Assignment 3/ComputeAllSimilarityFactors.m 0.67kB
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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= {}
}


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