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Bayesian Analysis with Python.
Annotation
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Packt Publishing,
2016.
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| Edición: | 1. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.
- 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.
- 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.
- 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.
- 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.