Bayesian inference (/ˈbeɪziən/ BAY-zee-ən or /ˈbeɪʒən/ BAY-zhən) is a method of statistical inference in which Bayes' theorem is used to update the probability...

various diseases. Efficient algorithms can perform inference and learning in Bayesian networks. Bayesian networks that model sequences of variables (e.g...

in Bayesian inference, Bayes' theorem can be used to estimate the parameters of a probability distribution or statistical model. Since Bayesian statistics...

Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning. They...

Bayesian inference of phylogeny combines the information in the prior and in the data likelihood to create the so-called posterior probability of trees...

as Bayesian inference.: 131  Mathematician Pierre-Simon Laplace pioneered and popularized what is now called Bayesian probability.: 97–98  Bayesian methods...

to account for the availability of related evidence. Bayesian inference is fundamental to Bayesian statistics. It has been considered to be "to the theory...

advocates of Bayesian inference assert that inference must take place in this decision-theoretic framework, and that Bayesian inference should not conclude...

and phylogeography. Approximate Bayesian computation can be understood as a kind of Bayesian version of indirect inference. Several efficient Monte Carlo...

enhance prediction accuracy. This principle integrates Bayesian inference with active inference, where actions are guided by predictions and sensory feedback...