Building probabilistic graphical models with Python : solve machine learning problems using probabilistic graphical models implemented in Python with realworld applications, Kiran R Karkera
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The instance Building probabilistic graphical models with Python : solve machine learning problems using probabilistic graphical models implemented in Python with realworld applications, Kiran R Karkera represents a material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries. This resource is a combination of several types including: Instance, Electronic.
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Building probabilistic graphical models with Python : solve machine learning problems using probabilistic graphical models implemented in Python with realworld applications, Kiran R Karkera
Resource Information
The instance Building probabilistic graphical models with Python : solve machine learning problems using probabilistic graphical models implemented in Python with realworld applications, Kiran R Karkera represents a material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries. This resource is a combination of several types including: Instance, Electronic.
 Label
 Building probabilistic graphical models with Python : solve machine learning problems using probabilistic graphical models implemented in Python with realworld applications, Kiran R Karkera
 Title remainder
 solve machine learning problems using probabilistic graphical models implemented in Python with realworld applications
 Statement of responsibility
 Kiran R Karkera
 Note
 Includes index
 Antecedent source
 unknown
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Contents

 Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Probability; The theory of probability; Goals of probabilistic inference; Conditional probability; The chain rule; The Bayes rule; Interpretations of probability; Random variables; Marginal distribution; Joint distribution; Independence; Conditional independence; Types of queries; Probability queries; MAP queries; Summary; Chapter 2: Directed Graphical Models; Graph terminology; Python digression; Independence and independent parameters; The Bayes network; The chain rule
 Reasoning patternsCausal reasoning; Evidential reasoning; Intercausal reasoning; Dseparation; The Dseparation example; Blocking and unblocking a Vstructure; Factorization and Imaps; The Naive Bayes model; The Naïve Bayes example; Summary; Chapter 3: Undirected Graphical Models; Pairwise Markov networks; The Gibbs distribution; An induced Markov network; Factorization; Flow of influence; Active trail and separation; Structured prediction; Problem of correlated features; The CRF representation; The CRF example; The factorizationindependence tango; Summary; Chapter 4: Structure Learning
 The structure learning landscapeConstraintbased structure learning; Part I; Part II; Part III; Summary of constraintbased approaches; Scorebased learning; The likelihood score; The Bayesian information criterion score; The Bayesian score; Summary of scorebased learning; Summary; Chapter 5: Parameter Learning; The likelihood function; Parameter learning example using MLE; MLE for Bayesian networks; Bayesian parameter learning example using MLE; Data fragmentation; Effect of data fragmentation on parameter estimation; Bayesian parameter estimation
 An example of Bayesian methods for parameter learningBayesian estimation for the Bayesian network; Example of Bayesian estimation; Summary; Chapter 6: Exact Inference Using Graphical Models; Complexity of inference; Realworld issues; Using the Variable Elimination algorithm; Marginalizing factors that are not relevant; Factor reduction to filter on evidence; Shortcomings of the bruteforce approach; Using the Variable Elimination approach; Complexity of Variable Elimination; Graph perspective; Learning the induced width from the graph structure; The tree algorithm
 The four stages of the junction tree algorithmUsing the junction tree algorithm for inference; Stage 1.1  moralization; Stage 1.2  triangulation; Stage 1.3  building the join tree; Stage 2  initializing potentials; Stage 3  message passing; Summary; Chapter 7: Approximate Inference Methods; The optimization perspective; Belief propagation on general graphs; Creating a cluster graph to run LBP; Message passing in LBP; Steps in the LBP algorithm; Improving the convergence of LBP; Applying LBP to segment an image; Understanding energybased models
 Control code
 882610549
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 unknown
 Extent
 1 online resource (iv, 155 pages)
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 unknown
 Form of item
 online
 Isbn
 9781306902878
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 unknown
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other physical details
 illustrations
 http://library.link/vocab/ext/overdrive/overdriveId
 cl0500000459
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 not applicable
 Record ID
 .b123888499
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 unknown
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 unknown sound
 Specific material designation
 remote
 System control number
 (OCoLC)882610549
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