PyMC3
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Getting started
Getting started with PyMC3
API quickstart
Variational API quickstart
Examples
API Reference
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Getting started
¶
Getting started with PyMC3
Abstract
Introduction
Installation
A Motivating Example: Linear Regression
Generating data
Model Specification
Model fitting
Maximum a posteriori methods
Sampling methods
Gradient-based sampling methods
Posterior analysis
Case study 1: Stochastic volatility
The Model
The Data
Model Specification
Fitting
Case study 2: Coal mining disasters
Arbitrary deterministics
Arbitrary distributions
Generalized Linear Models
Backends
Discussion
References
API quickstart
1. Model creation
2. Probability Distributions
Unobserved Random Variables
Observed Random Variables
Deterministic transforms
Automatic transforms of bounded RVs
Lists of RVs / higher-dimensional RVs
Initialization with test_values
3. Inference
3.1 Sampling
3.2 Analyze sampling results
3.3 Variational inference
4. Posterior Predictive Sampling
4.1 Predicting on hold-out data
Variational API quickstart
Basic setup
Tracking parameters
Replacements
basic usage
converting trace to Approximation
Multilabel logistic regression
Applying replacements in practice
Minibatches
Data
no minibatch inference
minibatch inference
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