krasserm/bayesian-machine-learning
Bayesian Machine Learning
Dieses Projekt ist eine Bildungs-Sammlung von Computational Notebooks und Tutorials mit Fokus auf Bayes'sches maschinelles Lernen und probabilistische Programmierung. Es bietet ein Framework für den Aufbau prädiktiver Modelle, die Unsicherheit repräsentieren, indem sie Wahrscheinlichkeitsverteilungen über Parameter definieren, anstatt sich auf einzelne Punktschätzungen zu verlassen.
Das Repository dient als Bibliothek statistischer Methoden zur Schätzung von Parameterverteilungen, zur Durchführung von Regressionen und zur Quantifizierung von Konfidenzniveaus in prädiktiven Systemen. Es deckt eine Reihe von Techniken ab, einschließlich Gauß-Prozess-Regression, Markov-Chain-Monte-Carlo-Sampling und Variationsinferenz zur Approximation komplexer Posterior-Verteilungen.
Über die Kern-Regression und -Inferenz hinaus demonstriert die Sammlung, wie latente Strukturen in hochdimensionalen Datensätzen identifiziert und die Suche nach optimalen Modellkonfigurationen durch probabilistische Surrogat-Modellierung automatisiert werden kann. Diese Ressourcen sind als Schritt-für-Schritt-Tutorials strukturiert, die die praktische Implementierung probabilistischer Modelle und Techniken zur Unsicherheitsquantifizierung erleichtern sollen.
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.
