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Mastering probabilistic graphical models using Python : master probablistic graphical models by learning through real-world problems and illustrative code examples in Python /
If you are a researcher or a machine learning enthusiast, or are working in the data science field and have a basic idea of Bayesian learning or probabilistic graphical models, this book will help you to understand the details of graphical models and use them in your data science problems.
| Clasificación: | Libro Electrónico |
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| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2015.
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| Colección: | Community experience distilled.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Bayesian Network Fundamentals; Probability theory; Random variable; Independence and conditional independence; Installing tools; IPython; pgmpy; Representing independencies using pgmpy; Representing joint probability distributions using pgmpy; Conditional probability distribution; Representing CPDs using pgmpy; Graph theory; Nodes and edges; Walk, paths, and trails; Bayesian models; Representation; Factorization of a distribution over a network
- Implementing Bayesian networks using pgmpyBayesian model representation; Reasoning pattern in Bayesian networks; D-separation; Direct connection; Indirect connection; Relating graphs and distributions; IMAP; IMAP to factorization; CPD representations; Deterministic CPDs; Context-specific CPDs; Tree CPD; Rule CPD; Summary; Chapter 2: Markov Network Fundamentals; Introducing the Markov network; Parameterizing a Markov network
- factor; Factor operations; Gibbs distributions and Markov networks; The factor graph; Independencies in Markov networks; Constructing graphs from distributions
- Bayesian networks and Markov networksConverting Bayesian models into Markov models; Converting Markov models into Bayesian models; Chordal graphs; Summary; Chapter 3: Inference
- Asking Questions to Models; Inference; Complexity of inference; Variable elimination; Analysis of variable elimination; Finding elimination ordering; Using the chordal graph property of induced graphs; Minimum fill/size/weight/search; Belief propagation; Clique tree; Constructing a clique tree; Message passing; Clique tree calibration; Message passing with division; Factor division
- Querying variables that are not in the same clusterMAP using variable elimination; Factor maximization; MAP using belief propagation; Finding the most probable assignment; Predictions from the model using pgmpy; A comparison of variable elimination and belief propagation; Summary; Chapter 4: Approximate Inference; The optimization problem; The energy function; Exact inference as an optimization; The propagation based approximation algorithm; Cluster graph belief propagation; Constructing cluster graphs; Pairwise Markov networks; Bethe cluster graph; Propagation with approximate messages
- Message creationInference with approximate messages; Sum-product expectation propagation; Belief update propagation; Sampling-based approximate methods; Forward sampling; Conditional probability distribution; Likelihood weighting and importance sampling; Importance sampling; Importance sampling in Bayesian networks; Computing marginal probabilities; Ratio likelihood weighting; Normalized likelihood weighting; Markov chain Monte Carlo methods; Gibbs sampling; Markov chains; The multiple transitioning model; Using a Markov chain; Collapsed particles; Collapsed importance sampling; Summary
- Chapter 5: Model Learning
- Parameter Estimation in Bayesian Networks