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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 |
|---|---|
| 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) |
MARC
| LEADER | 00000cam a2200000Ii 4500 | ||
|---|---|---|---|
| 001 | OR_ocn922580777 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr unu|||||||| | ||
| 008 | 150930s2015 enka o 001 0 eng d | ||
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| 020 | |z 9781784394684 | ||
| 020 | |a 9781784395216 | ||
| 020 | |a 1784395218 | ||
| 020 | |a 1784394688 | ||
| 020 | |a 9781784394684 | ||
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| 029 | 1 | |a GBVCP |b 882744704 | |
| 035 | |a (OCoLC)922580777 | ||
| 037 | |a CL0500000653 |b Safari Books Online | ||
| 050 | 4 | |a QA279 | |
| 082 | 0 | 4 | |a 519.5 |2 23 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Ankan, Ankur, |e author. | |
| 245 | 1 | 0 | |a Mastering probabilistic graphical models using Python : |b master probablistic graphical models by learning through real-world problems and illustrative code examples in Python / |c Ankur Ankan, Abinash Panda. |
| 246 | 3 | 0 | |a Master probablistic graphical models by learning through real-world problems and illustrative code examples in Python |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2015. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Community experience distilled | |
| 588 | |a Description based on online resource; title from cover page (Safari, viewed September 28, 2015). | ||
| 500 | |a Includes index. | ||
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a Chapter 5: Model Learning -- Parameter Estimation in Bayesian Networks | |
| 520 | |a 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. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Graphical modeling (Statistics) | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 6 | |a Modèles graphiques (Statistique) | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 7 | |a Graphical modeling (Statistics) |2 fast |0 (OCoLC)fst00946659 | |
| 650 | 7 | |a Python (Computer program language) |2 fast |0 (OCoLC)fst01084736 | |
| 700 | 1 | |a Panda, Abinash, |e author. | |
| 830 | 0 | |a Community experience distilled. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781784394684/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 994 | |a 92 |b IZTAP | ||