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Recursive Bayesian estimation
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<p>In <a href="/facts/Probability_Theory/mFBn51rE">probability theory</a>, <a href="/facts/Statistics/DNPsKGYU">statistics</a>, and <a href="/facts/Machine_Learning/e0w0XJTu">machine learning</a>, recursive Bayesian estimation, also known as a Bayes filter, is a general probabilistic approach for <a href="/facts/Density_estimation/YthqJssX">estimating</a> an unknown <a href="/facts/Probability_density_function/zvfybna4">probability density function</a> (<a href="/facts/Probability_density_function/zvfybna4">PDF</a>) recursively over time using incoming measurements and a mathematical process model. The process relies heavily upon mathematical concepts and models that are theorized within a study of prior and posterior probabilities known as <a href="/facts/Bayesian_statistics/9w7b1Bw4">Bayesian statistics</a>. </p>