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Modern Probabilistic Modeler Original
Introduction
Essential Info and Links
A Generative Refresher on Probability and Bayes
A Generative Primer on Probability
Ground truth vs. Canonical Probability Distributions (12:05)
Revisiting Bayes via Training Machine Learning Algorithms
Revisiting Bayes via Machine Learning In Production
Model-based Thinking
Towards Model-based Machine Learning (7:41)
Modeling the Data Generating Process (6:30)
Generative Probability Models as Explanations of Data
Building a Model as a Directed Graph (1:16)
Probability and the DAG (1:46)
Probabilistic Programming
Probabilistic Machine Learning
Probabilistic Programming Defined
Execution, Sampling, Conditioning
Pros and Cons of Various Probabilistic Programming Approaches
PPL Landscape and Deep Probabilistic Programming
Computational Bayes
Anatomy of a Bayesian Program
Stochastic simulation and Bayesian computation
Visual Model Diagnostics
Monte Carlo Reasoning on the Posterior
Predictive Checks
The Bayesian Model Building Workflow
Model Checking
Predictive Checks
Information Criteria and Cross-Validation
Making domain knowledge consistent with the model assumptions
Shannon's Model of Communication (5:44)
The Map is Not the Territory: Separating the Receiver and Transmitter (5:29)
Transmitter-Receiver Case Study (4:09)
Technical Variation and Gemba
Hierarchical and Latent Variable Models
Hierarchy and Heterogeneity: A Case Study
Mixed Model, Random Effects and the Notorious 8 Schools
Exchangeability, de Finetti, and Causality
Partial Pooling
Thinking Causally about Hierarchy
Common Classes of Latent Variable Models
Bayesian Decision Theory
Bayesian Risk: Connect Data to Decisions
Bayesian Decision Theory
Statistical Hypothesis Testing, Bayes Rules, and Admissibility
Bayesian Thompson Sampling
Primer on Approximate Inference with Automatic Differentiation
Primer on Approximate Inference
Stochastic Variational Inference
Simulation-based inference with the GAN approach
Pros and Cons of Various Probabilistic Programming Approaches
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