CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
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Probabilistic Programming And Bayesian Methods For Hackers
This project is a computational statistics textbook and Bayesian data analysis course. It serves as a guide for performing statistical inference and quantifying uncertainty through a probabilistic programming workflow using Python.
The resource employs a computation-first pedagogy, teaching Bayesian methods and parameter estimation through executable code and simulations instead of formal mathematical notation. It provides a practical approach to implementing Markov Chain Monte Carlo sampling to estimate posterior distributions.
The content covers building probabilistic models, integrating expert priors, and performing Bayesian inference. It also includes methods for decision optimization under uncertainty by applying loss functions to probabilistic estimates to determine the most beneficial actions based on the costs of error.
The material is delivered as a series of Jupyter Notebooks.
Features
- Probabilistic Models - Builds probabilistic models that represent real-world uncertainty through random variables and conditional distributions.
- Probabilistic Programming - Provides a comprehensive guide to performing statistical inference and uncertainty quantification using a probabilistic programming workflow.
- Markov Chain Monte Carlo Sampling - Implements Markov Chain Monte Carlo algorithms to estimate posterior distributions from probabilistic models.
- Parameter Estimation Methods - Implements Markov Chain Monte Carlo sampling to estimate posterior distributions and model parameters.
- Simulation-Driven Inference - Derives statistical conclusions by running thousands of random trials to approximate the behavior of theoretical mathematical models.
- Probabilistic Modeling - Creates probabilistic models to infer hidden patterns in data and calculate event probabilities.
- Bayesian Inference - Estimates unknown parameters of a model based on observed data and prior beliefs.
- Probabilistic Programming - Builds mathematical models of real-world processes to estimate unknown parameters via a probabilistic programming workflow.
- MCMC Sampling - Generates samples from a posterior distribution and provides tools to verify simulation convergence.
- Computation-First Pedagogies - Employs a computation-first pedagogy that teaches Bayesian methods through executable code and simulations instead of formal notation.
- Bayesian Estimation Guides - Offers an educational introduction to Bayesian methods and parameter estimation using executable Python code.
- Statistics Courses - Provides a comprehensive instructional program for Bayesian data analysis and statistical inference.
- Textbooks - Serves as an open-source educational textbook integrating Bayesian theory with practical Python simulations.
- Statistical Estimation - Integrates expert priors and observed data to refine parameter estimates.
- Decision-Based Loss Optimization - Applies loss functions to probabilistic estimates to determine the most beneficial action based on the cost of error.
- Decision Optimization - Provides methods for decision optimization under uncertainty by applying loss functions to probabilistic estimates.
- Optimal Action Estimation - Determines the most beneficial actions under uncertainty by estimating the value of decisions.
- Probabilistic Model Optimization - Applies loss functions to probabilistic estimates to determine the most beneficial decisions based on costs of error.
- Probabilistic Priors - Integrates prior beliefs and expert opinions into probabilistic models to refine estimates.
- From-Scratch Implementations - Uses Python to build probabilistic models from scratch without relying on specialized statistical software.
- Bayesian Machine Learning - Practical guide to probabilistic programming and Bayesian inference.
- Probabilistic Modeling - Introductory Bayesian methods for hackers.
- Bayesian Statistics - Interactive guide to probabilistic programming.
- Data Processing - Interactive guide to probabilistic programming and Bayesian methods.
- Data Science - Introduction to Bayesian methods using a computational, code-first approach.
- Programming Languages - Introduction to Bayesian methods using Python.
- Educational Books - Practical introduction to Bayesian methods and probabilistic programming techniques.
- Learning & Reference - Introduction to Bayesian methods and probabilistic programming.
- Mathematics and Statistics - Resource for Bayesian methods in Python.
