Learn IT - Books tags bayesian

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...

Bayesian Networks in R: with Applications in Systems Biology (Use R!)

Springer, 2013

While there have been significant advances in capturing data from the entities across complex real-world systems, their associations and relationships are largely unknown. Associations between the entities may reveal interesting system-level properties that may not be apparent otherwise. Often these associations are hypothesized...

The R Book

John Wiley & Sons, 2012

Hugely successful and popular text presenting an extensive and comprehensive guide for all R users

The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help...

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.

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Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics (Studies in Computational Intelligence)

Springer, 2019

This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. The overall text contains many interesting results that are worth applying in practice, while it is also a source of intriguing and motivating questions for advanced research on spatial data science.

The...

Mathematical Statistics with Resampling and R

John Wiley & Sons, 2011

This book bridges the latest software applications with the benefits of modern resampling techniques

Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques...

Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs)

Cambridge University Press, 2016

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. “Big data,” “data science,” and “machine learning” have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science...

Bayesian Reasoning and Machine Learning

Cambridge University Press, 2012

We live in a world that is rich in data, ever increasing in scale. This data comes from many dierent sources in science (bioinformatics, astronomy, physics, environmental monitoring) and commerce (customer databases, nancial transactions, engine monitoring, speech recognition, surveillance, search). Possessing the knowledge as to...

Python for Finance: Analyze Big Financial Data

O'Reilly, 2014

The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects...

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...

Adaptive Design Theory and Implementation Using SAS and R (Chapman & Hall/CRC Biostatistics Series)

CRC Press, 2007

Adaptive design has become an important tool in modern pharmaceutical research and development. Compared to a classic trial design with static features, an adaptive design allows for the modification of the characteristics of ongoing trials based on cumulative information. Adaptive designs increase the probability of success, reduce costs...

An Introduction to Game Theory

Oxford University Press, 2003

Game-theoretic reasoning pervades economic theory and is used widely in other social and behavioral sciences. An Introduction to Game Theory, by Martin J. Osborne, presents the main principles of game theory and shows how they can be used to understand economic, social, political, and biological phenomena. The book introduces in an...

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