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A Second Course in Probability
A Second Course in Probability

The 2006 INFORMS Expository Writing Award-winning and best-selling author Sheldon Ross (University of Southern California) teams up with Erol Peköz (Boston University) to bring you this textbook for undergraduate and graduate students in statistics, mathematics, engineering, finance, and actuarial science. This is a guided tour designed...

Markov Random Field Modeling in Image Analysis (Advances in Pattern Recognition)
Markov Random Field Modeling in Image Analysis (Advances in Pattern Recognition)

Modeling problems in this book are addressed mainly from the computational viewpoint. The primary concerns are how to define an objective function for the optimal solution to a image analysis or computer vision problem and how to find the optimal solution. The solution is defined in an optimization sense because the perfect solution is...

Python Reinforcement Learning Projects: Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow
Python Reinforcement Learning Projects: Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow

Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful libraries

Key Features

  • Implement Q-learning and Markov models with Python and OpenAI
  • Explore the power of TensorFlow to build self-learning models
  • Eight AI...
A First Course in Stochastic Models
A First Course in Stochastic Models
The teaching of appliedprobability needs a fresh approach. The fieldof applied probability has changedprofound ly in the past twenty years andyet the textbooks in use today do not fully reflect the changes. The development of computational methods has greatly contributed to a better understanding of the theory. It is my conviction that theory is...
Continuous-Time Markov Chains and Applications: A Two-Time-Scale Approach (Stochastic Modelling and Applied Probability)
Continuous-Time Markov Chains and Applications: A Two-Time-Scale Approach (Stochastic Modelling and Applied Probability)

This book gives a systematic treatment of singularly perturbed systems that naturally arise in control and optimization, queueing networks, manufacturing systems, and financial engineering. It presents results on asymptotic expansions of solutions of Komogorov forward and backward equations, properties of functional occupation measures,...

Python Machine Learning Cookbook
Python Machine Learning Cookbook

100 recipes that teach you how to perform various machine learning tasks in the real world

About This Book

  • Understand which algorithms to use in a given context with the help of this exciting recipe-based guide
  • Learn about perceptrons and see how they are used to build neural...
Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models
Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models

Explore and master the most important algorithms for solving complex machine learning problems.

Key Features

  • Discover high-performing machine learning algorithms and understand how they work in depth
  • One-stop solution to mastering supervised, unsupervised, and...
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks

Bayesian networks have received a lot of attention over the last few decades from both scientists and engineers, and across a number of fields, including artificial intelligence (AI), statistics, cognitive science, and philosophy.

Perhaps the largest impact that Bayesian networks have had is on the field of AI, where they were...

The Variational Bayes Method in Signal Processing
The Variational Bayes Method in Signal Processing
This is the first book-length treatment of the Variational Bayes (VB) approximation in signal processing. It has been written as a self-contained, self-learning guide for academic and industrial research groups in signal processing, data analysis, machine learning, identification and control. It reviews the VB distributional approximation, showing...
Applied Bayesian Statistics: With R and OpenBUGS Examples (Springer Texts in Statistics)
Applied Bayesian Statistics: With R and OpenBUGS Examples (Springer Texts in Statistics)

This book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs  in Statistics, Biostatistics, Engineering, Economics,...

Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem
Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem

Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn

Key Features

  • Build a variety of Hidden Markov Models (HMM)
  • Create and apply models to any sequence of data to analyze, predict, and extract valuable...
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)

Master Bayesian Inference through Practical Examples and Computation–Without Advanced Mathematical Analysis

 

Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely...

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