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Everything about Bayesian programming
Statistics concept
Bayesian programming is a formalism and a methodology for having a technique to specify probabilistic models and solve problems when less than the necessary information is available. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Bayesian programmingA 5-minute orientation
- Skim a recent paper: Applying Bayesian Analysis Guidelines to Empirical Software Engineering Data: The Case of Programming Languages and Code QualityInfluential work since 2022
- Pick up a classic book: Statistical RethinkingThe most-reprinted book on the subject
- Read the landmark paper: Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary ProgrammingCited 157 times
- Go sideways: Recursive Bayesian estimationA closely related topic
- Go sideways: Naive Bayes classifierA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary Programming
Integer Linear Programming for the Bayesian network structure learning problem
Value of Seasonal Flow Forecasts in Bayesian Stochastic Programming
Bayesian Robot Programming
Bayesian dynamic programming
Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming
Bayesian training of neural networks using genetic programming
Estimating programming knowledge with Bayesian knowledge tracing
🆕 Influential since 2022
Applying Bayesian Analysis Guidelines to Empirical Software Engineering Data: The Case of Programming Languages and Code Quality
A signomial programming-based approach for multi-echelon supply chain disruption risk assessment with robust dynamic Bayesian network
Static Posterior Inference of Bayesian Probabilistic Programming via Polynomial Solving
Bayesian dynamic programming approach for tracking time-varying model properties in SHM
Distributed ANN-bi level two-stage stochastic fuzzy possibilistic programming with Bayesian model for irrigation scheduling management
📚 Books worth reading
Statistical RethinkingRichard McElreath · 2015
Probability and statistics with RMaría Dolores Ugarte, Maria Dolores Ugarte · 2008
Bayesian Analysis with PythonOsvaldo Martin · 2016
Bayesian Analysis with R for Drug DevelopmentHarry Yang, Steven Novick · 2019
Doing Bayesian Data AnalysisJohn K. Kruschke · 2010- 📕Bayesian Modeling and Computation in PythonOsvaldo A. Martin, Ravin Kumar · 2021
- 📕Bayesian Approaches in Oncology Using R and OpenBUGSAtanu Bhattacharjee · 2020
Think BayesAllen B. Downey · 2013
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Recursive Bayesian estimation In probability theory, statistics, and machine learning, recursive Bayesian estimation, also known as a Bayes filter, is a general probabilistic approach…
- Naive Bayes classifier In statistics, naive (sometimes simple or idiot's) Bayes classifiers are a family of "probabilistic classifiers" which assume that the features are conditionall…
- Particle filter Particle filters, also known as sequential Monte Carlo methods, are a set of Monte Carlo algorithms used to find approximate solutions for filtering problems…
- Variational Bayesian methods Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning. They…
- Bayesian network A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a…
- Variable-order Markov model In the mathematical theory of stochastic processes, variable-order Markov (VOM) models are an important class of models that extend the well known Markov…
- Graphical model A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional…
- Probabilistic programming Probabilistic programming (PP) is a programming paradigm based on the declarative specification of probabilistic models, for which inference is performed…