krasserm/bayesian-machine-learning
Bayesian Machine Learning
This project is an educational collection of computational notebooks and tutorials focused on Bayesian machine learning and probabilistic programming. It provides a framework for building predictive models that represent uncertainty by defining probability distributions over parameters rather than relying on single point estimates.
The repository serves as a library of statistical methods for estimating parameter distributions, performing regression, and quantifying confidence levels in predictive systems. It covers a range of techniques including Gaussian process regression, Markov chain Monte Carlo sampling, and variational inference to approximate complex posterior distributions.
Beyond core regression and inference, the collection demonstrates how to identify latent structures in high-dimensional datasets and automate the search for optimal model configurations through probabilistic surrogate modeling. These resources are structured as step-by-step tutorials designed to facilitate the practical implementation of probabilistic models and uncertainty quantification techniques.
Features
- Bayesian Machine Learning - Provides a comprehensive collection of computational notebooks and tutorials for learning and implementing Bayesian machine learning techniques.
- Gaussian Processes - Defines probability distributions over functions to predict continuous values while providing a rigorous measure of uncertainty for every output.
- Markov Chain Monte Carlo Sampling - Generates sequences of random samples from complex probability distributions to approximate integrals that are otherwise computationally intractable.
- Predictive Modeling - Builds machine learning models that represent uncertainty by defining probability distributions over parameters instead of relying on single point estimates.
- Probabilistic Models - Builds predictive models that represent uncertainty by defining probability distributions over parameters rather than relying on single point estimates.
- Uncertainty Estimation - Measures the confidence levels of machine learning predictions to ensure models provide reliable estimates of their own potential errors.
- Variational Inference - Transforms difficult posterior distribution calculations into an optimization problem to estimate model parameters efficiently without exhaustive sampling.
- Probabilistic Programming - Offers practical tutorials on defining probability distributions over model parameters to quantify uncertainty in predictive systems.
- Latent Structure Analyzers - Identifies underlying patterns in complex datasets using statistical techniques to simplify high-dimensional information.
- Probability Density Functions - Estimates outcomes and classifies data points by defining probability distributions over possible functions while utilizing efficient mathematical approximations.
- Bayesian Regressions - Projects input data into higher-dimensional feature spaces to perform linear regression while maintaining full probabilistic control over parameter uncertainty.
- Surrogate Model Optimization - Uses statistical models to approximate expensive objective functions, enabling efficient search and optimization of hyperparameters in high-dimensional spaces.
- Probabilistic Graphical Models - Represents complex dependencies between variables as directed acyclic graphs to perform inference on hidden structures within high-dimensional datasets.
- Hyperparameter Optimization - Searches for the most effective model settings by iteratively testing different configurations through probabilistic surrogate models.
- Bayesian Optimization - Automates the search for optimal model configurations by using probabilistic surrogate models to guide the selection process.
- Bayesian Neural Network Implementations - Measures the confidence of machine learning predictions by applying probabilistic methods to weight distributions.
- Statistical Modeling Libraries - Ships a library of statistical methods for estimating parameter distributions and performing regression using Bayesian inference.
Star history
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