---
product_id: 1703925
title: "Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)"
price: "₹ 23373"
currency: INR
in_stock: true
reviews_count: 13
url: https://www.desertcart.in/products/1703925-probabilistic-graphical-models-principles-and-techniques-adaptive-computation-and-machine
store_origin: IN
region: India
---

# Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)

**Price:** ₹ 23373
**Availability:** ✅ In Stock

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- **What is this?** Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
- **How much does it cost?** ₹ 23373 with free shipping
- **Is it available?** Yes, in stock and ready to ship
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## Description

A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

Review: the standard probabilistic graphical model book. must have for reference - great book. must read for machine learning and a good reference book
Review: A Superb Book - If you want a very close look under the hood of Bayesian Networks, I can highly recommend Probabilistic Graphical Models. It's extremely comprehensive (1,200+ pages), well structured and clearly written. Theory, computation and application - including how to think about causation - are all covered in depth. Not light reading and not suited for those with limited stats background, but all in all one of the best textbooks on analytics topics I've ever read. Very impressive.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #856,614 in Books ( See Top 100 in Books ) #329 in Natural Language Processing (Books) #742 in Probability & Statistics (Books) #1,666 in Artificial Intelligence & Semantics |
| Customer Reviews | 4.5 out of 5 stars 124 Reviews |

## Images

![Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) - Image 1](https://m.media-amazon.com/images/I/71py9uisI6L.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ the standard probabilistic graphical model book. must have for reference
*by T***R on July 26, 2025*

great book. must read for machine learning and a good reference book

### ⭐⭐⭐⭐⭐ A Superb Book
*by C***C on September 2, 2013*

If you want a very close look under the hood of Bayesian Networks, I can highly recommend Probabilistic Graphical Models. It's extremely comprehensive (1,200+ pages), well structured and clearly written. Theory, computation and application - including how to think about causation - are all covered in depth. Not light reading and not suited for those with limited stats background, but all in all one of the best textbooks on analytics topics I've ever read. Very impressive.

### ⭐⭐⭐⭐⭐ A great reference book for PGM
*by E***2 on March 13, 2019*

This is the textbook for my PGM class. It is definitely not an easy book to read, but its content is very comprehensive. It is a great reference to get more details of PGM. I highly recommend this book!

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*Store origin: IN*
*Last updated: 2026-06-03*