|
|
|
|
| LEADER |
03226nmm a2200433 u 4500 |
| 001 |
EB001910439 |
| 003 |
EBX01000000000000001073341 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
210123 ||| eng |
| 020 |
|
|
|a 1789349664
|
| 050 |
|
4 |
|a QA76.73.P98
|
| 100 |
1 |
|
|a Martin, Osvaldo
|
| 245 |
0 |
0 |
|a Bayesian analysis with Python
|b introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ
|c Osvaldo Martin
|
| 246 |
3 |
1 |
|a Unleash the power and flexibility of the Bayesian framework
|
| 250 |
|
|
|a Second edition
|
| 260 |
|
|
|a Birmingham, UK
|b Packt Publishing
|c 2018
|
| 300 |
|
|
|a 1 volume
|b illustrations
|
| 505 |
0 |
|
|a Bayesian analysis with Python : introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ -- Dedication -- Foreword -- Contributors -- Table of Contents -- Foreword -- Chapter 1: Thinking Probabilistically -- Chapter 2: Programming Probabilistically -- Chapter 3: Modeling with Linear Regression -- Chapter 4: Generalizing Linear Models -- Chapter 5: Model Comparison -- Chapter 6: Mixture Models -- Chapter 7: Gaussian Processes -- Chapter 8: Inference Engines -- Chapter 9: Where To Go Next? -- Other Books You May Enjoy -- Index
|
| 653 |
|
|
|a Bayesian statistical decision theory / http://id.loc.gov/authorities/subjects/sh85012506
|
| 653 |
|
|
|a Python (Computer program language) / fast
|
| 653 |
|
|
|a Natural Language Processing
|
| 653 |
|
|
|a Python (Computer program language) / http://id.loc.gov/authorities/subjects/sh96008834
|
| 653 |
|
|
|a Théorie de la décision bayésienne
|
| 653 |
|
|
|a Bayesian statistical decision theory / fast
|
| 653 |
|
|
|a Traitement automatique des langues naturelles
|
| 653 |
|
|
|a Natural language processing (Computer science) / fast
|
| 653 |
|
|
|a Python (Langage de programmation)
|
| 653 |
|
|
|a Natural language processing (Computer science) / http://id.loc.gov/authorities/subjects/sh88002425
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b OREILLY
|a O'Reilly
|
| 500 |
|
|
|a Includes index
|
| 015 |
|
|
|a GBB9D2289
|
| 776 |
|
|
|z 9781789341652
|
| 776 |
|
|
|z 9781789349665
|
| 856 |
4 |
0 |
|u https://learning.oreilly.com/library/view/~/9781789341652/?ar
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 005.133
|
| 082 |
0 |
|
|a 500
|
| 520 |
|
|
|a The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to
|