krasserm/bayesian-machine-learning

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.

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