Learn IT - Books tags bayesian

Think Stats

O'Reilly, 2011

If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

You'll work with a case study throughout the...

Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks

No Starch Press, 2019

Fun guide to learning Bayesian statistics and probability through unusual and illustrative examples.

Probability and statistics are increasingly important in a huge range of professions. But many people use data in ways they don't even understand, meaning they aren't getting the most from it. Bayesian...

Probability: With Applications and R

John Wiley & Sons, 2013

An introduction to probability at the undergraduate level

Chance and randomness are encountered on a daily basis. Authored by a highly qualified professor in the field, Probability: With Applications and R delves into the theories and applications essential to obtaining a thorough understanding of probability.

...

Machine Learning with R - Second Edition

Packt Publishing, 2015

Key Features

Harness the power of R for statistical computing and data science
Explore, forecast, and classify data with R
Use R to apply common machine learning algorithms to real-world scenarios

Book Description

Machine learning, at its core, is concerned...

Blind Image Deconvolution: Theory and Applications

CRC Press, 2007

Blind image deconvolution is constantly receiving increasing attention from the academic as well the industrial world due to both its theoretical and practical implications. The field of blind image deconvolution has several applications in different areas such as image restoration, microscopy, medical imaging, biological imaging, remote sensing,...

Biomedical Image Analysis: Tracking (Synthesis Lectures on Image, Video, & Multimedia Processing)

Morgan and Claypool Publishers, 2006

In biological and medical imaging applications, tracking objects in motion is a critical task. This book describes the state-of-the-art in biomedical tracking techniques. We begin by detailing methods for tracking using active contours, which have been highly successful in biomedical applications. The book next covers the major probabilistic...

Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification

No Starch Press, 2005

Join author John Zdziarski for a look inside the brilliant minds that have conceived clever new ways to fight spam in all its nefarious forms. This landmark title describes, in-depth, how statistical filtering is being used by next-generation spam filters to identify and filter unwanted messages, how spam filtering works and how language...

Advances in Mathematical Modeling for Reliability

IOS Press, 2008

Advances in Mathematical Modeling for Reliability discusses fundamental issues on mathematical modeling in reliability theory and its applications. Beginning with an extensive discussion of graphical modeling and Bayesian networks, the focus shifts towards repairable systems: a discussion about how sensitive availability calculations parameter...

Think Bayes

O'Reilly, 2013

If you know how to program with Python and also know a little about probability, you’re ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you...

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

Packt Publishing, 2018

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 Argumentation: The practical side of probability (Synthese Library)

Springer, 2012

Relevant to, and drawing from, a range of disciplines, the chapters in this collection show the diversity, and applicability, of research in Bayesian argumentation. Together, they form a challenge to philosophers versed in both the use and criticism of Bayesian models who have largely overlooked their potential in argumentation. Selected from...

Machine Learning for Finance: Principles and practice for financial insiders

Packt Publishing, 2019

A guide to advances in machine learning for financial professionals, with working Python code

Key Features

Explore advances in machine learning and how to put them to work in financial industries

Clear explanation and expert discussion of how machine learning works, with...

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