Cargando…
Bayesian Analysis with Python.
Annotation
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Packt Publishing,
2016.
|
| Edición: | 1. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a22000007a 4500 | ||
|---|---|---|---|
| 001 | EBOOKCENTRAL_ocn968635488 | ||
| 003 | OCoLC | ||
| 005 | 20240329122006.0 | ||
| 006 | m o d | ||
| 007 | cr ||||||||||| | ||
| 008 | 161202s2016 xx o 000 0 eng d | ||
| 040 | |a IDEBK |b eng |e pn |c IDEBK |d COO |d OCLCQ |d EBLCP |d MERUC |d REB |d CHVBK |d OCLCQ |d OCLCO |d OCLCF |d OCLCQ |d OCLCO |d K6U |d OCLCQ |d OCLCO |d OCLCL | ||
| 020 | |a 1785889850 |q (ebk) | ||
| 020 | |a 9781785889851 | ||
| 029 | 1 | |a AU@ |b 000066229930 | |
| 029 | 1 | |a CHNEW |b 000949254 | |
| 029 | 1 | |a CHVBK |b 483154296 | |
| 035 | |a (OCoLC)968635488 | ||
| 037 | |a 972880 |b MIL | ||
| 050 | 4 | |a T55.4-60.8 | |
| 082 | 0 | 4 | |a 519.5 |2 23 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Osvaldo Martin. | |
| 245 | 1 | 0 | |a Bayesian Analysis with Python. |
| 250 | |a 1. | ||
| 260 | |b Packt Publishing, |c 2016. | ||
| 300 | |a 1 online resource (282) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 505 | 0 | |a Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Thinking Probabilistically -- A Bayesian Inference Primer; Statistics as a form of modeling; Exploratory data analysis; Inferential statistics; Probabilities and uncertainty; Probability distributions; Bayes' theorem and statistical inference; Single parameter inference; The coin-flipping problem; The general model; Choosing the likelihood; Choosing the prior; Getting the posterior; Computing and plotting the posterior; Influence of the prior and how to choose one. | |
| 505 | 8 | |a Communicating a Bayesian analysisModel notation and visualization; Summarizing the posterior; Highest posterior density; Posterior predictive checks; Installing the necessary Python packages; Summary; Exercises; Chapter 2: Programming Probabilistically -- A PyMC3 Primer; Probabilistic programming; Inference engines; Non-Markovian methods; Markovian methods; PyMC3 introduction; Coin-flipping, the computational approach; Model specification; Pushing the inference button; Diagnosing the sampling process; Summarizing the posterior; Posterior-based decisions; ROPE; Loss functions; Summary. | |
| 505 | 8 | |a Keep readingExercises; Chapter 3: Juggling with Multi-Parametric and Hierarchical Models; Nuisance parameters and marginalized distributions; Gaussians, Gaussians, Gaussians everywhere; Gaussian inferences; Robust inferences; Student's t-distribution; Comparing groups; The tips dataset; Cohen's d; Probability of superiority; Hierarchical models; Shrinkage; Summary; Keep reading; Exercises; Chapter 4: Understanding and Predicting Data with Linear Regression Models; Simple linear regression; The machine learning connection; The core of linear regression models. | |
| 505 | 8 | |a Linear models and high autocorrelationModifying the data before running; Changing the sampling method; Interpreting and visualizing the posterior; Pearson correlation coefficient; Pearson coefficient from a multivariate Gaussian; Robust linear regression; Hierarchical linear regression; Correlation, causation, and the messiness of life; Polynomial regression; Interpreting the parameters of a polynomial regression; Polynomial regression -- the ultimate model?; Multiple linear regression; Confounding variables and redundant variables; Multicollinearity or when the correlation is too high. | |
| 505 | 8 | |a Masking effect variablesAdding interactions; The GLM module; Summary; Keep reading; Exercises; Chapter 5: Classifying Outcomes with Logistic Regression; Logistic regression; The logistic model; The iris dataset; The logistic model applied to the iris dataset; Making predictions; Multiple logistic regression; The boundary decision; Implementing the model; Dealing with correlated variables; Dealing with unbalanced classes; How do we solve this problem?; Interpreting the coefficients of a logistic regression; Generalized linear models; Softmax regression or multinomial logistic regression. | |
| 520 | 8 | |a Annotation |b Unleash the power and flexibility of the Bayesian frameworkAbout 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 ForStudents, 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 frameworkIn DetailThe 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 approachBayes 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. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Python. | |
| 650 | 0 | |a Natural language processing. | |
| 650 | 0 | |a Bayesian statistical decision theory. | |
| 650 | 6 | |a Théorie de la décision bayésienne. | |
| 650 | 7 | |a Bayesian statistical decision theory |2 fast | |
| 758 | |i has work: |a Bayesian analysis with Python (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGhYfT93pmGp49GHcDJ8P3 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=4749335 |z Texto completo |
| 936 | |a BATCHLOAD | ||
| 938 | |a EBL - Ebook Library |b EBLB |n EBL4749335 | ||
| 938 | |a ProQuest MyiLibrary Digital eBook Collection |b IDEB |n cis35312829 | ||
| 994 | |a 92 |b IZTAP | ||