LEADER 04091nam 2200493 450 001 9910814992103321 005 20161216113838.0 010 $a1-78588-985-0 035 $a(CKB)3710000000972423 035 $a(MiAaPQ)EBC4749335 035 $z(PPN)220202591 035 $a(CaSebORM)9781785883804 035 $a(PPN)196934346 035 $a(EXLCZ)993710000000972423 100 $a20170227h20162016 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aBayesian analysis with Python $eunleash the power and flexibility of the Bayesian framework /$fOsvaldo Martin 205 $a1st edition 210 1$aBirmingham, England ;$aMumbai, [India] :$cPackt,$d2016. 210 4$dİ2016 215 $a1 online resource (282 pages) $cillustrations (some color), graphs 300 $aIncludes index. 311 $a1-78588-380-1 330 $aUnleash the power and flexibility of the Bayesian framework About This Book Simplify the Bayes process for solving complex statistical problems using Python; Tutorial guide that will take the you through the journey of Bayesian analysis with the help of sample problems and practice exercises; Learn how and when to use Bayesian analysis in your applications with this guide. Who This Book Is For Students, researchers and data scientists who wish to learn Bayesian data analysis with Python and implement probabilistic models in their day to day projects. Programming experience with Python is essential. No previous statistical knowledge is assumed. What You Will Learn Understand the essentials Bayesian concepts from a practical point of view Learn how to build probabilistic models using the Python library PyMC3 Acquire the skills to sanity-check your models and modify them if necessary Add structure to your models and get the advantages of hierarchical models Find out how different models can be used to answer different data analysis questions When in doubt, learn to choose between alternative models. Predict continuous target outcomes using regression analysis or assign classes using logistic and softmax regression. Learn how to think probabilistically and unleash the power and flexibility of the Bayesian framework In Detail The purpose of this book is to teach the main concepts of Bayesian data analysis. We will learn how to effectively use PyMC3, a Python library for probabilistic programming, to perform Bayesian parameter estimation, to check models and validate them. This book begins presenting the key concepts of the Bayesian framework and the main advantages of this approach from a practical point of view. Moving on, we will explore the power and flexibility of generalized linear models and how to adapt them to a wide array of problems, including regression and classification. We will also look into mixture models and clustering data, and we will finish with advanced topics like non-parametrics models and Gaussian processes. With the help of Python and PyMC3 you will learn to implement, check and expand Bayesian models to solve data analysis problems. Style and approach Bayes algorithms are widely used in statistics, machine learning, artificial intelligence, and data mining. This will be a practical guide allowing the readers to use Bayesian methods for statistical modelling and analysis using Python. Downloading the example code for this b... 517 3 $aUnleash the power and flexibility of the Bayesian framework 606 $aPython (Computer program language) 606 $aNatural language processing (Computer science) 606 $aBayesian statistical decision theory 615 0$aPython (Computer program language) 615 0$aNatural language processing (Computer science) 615 0$aBayesian statistical decision theory. 676 $a005.133 700 $aMartin$b Osvaldo$01604405 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910814992103321 996 $aBayesian analysis with Python$93929219 997 $aUNINA