"Prior Knowledge answers your company's most pressing questions using advanced probabilistic inference technology."

"founded in 1992 to promote the development and application of Bayesian analysis useful in the solution of theoretical and applied problems in science, industry and government"

DP, IBP, etc.

"Bayes is a very limited special case of the replicator equation"

A scoring method for eliciting truthful subjective data in situations where objective truth is unknowable. The method assigns high scores not to the most common answers but to the answers that are more common than collectively predicted.

Introduces Dirichlet processes and describe different representations of Dirichlet processes, including the Blackwell-MacQueen urn scheme, Chinese restaurant processes, and the stick-breaking construction.

A theory that relates biophysical properties of single neurons to principles of Bayesian probability theory, reinforcement learning and efficient coding.

"I think the predominant philosophy [in the mid of the 21st century] will be a Bayes/non-Bayes synthesis or compromise, and that the Bayesian part will be mostly hierarchical." -- I. J. Good, 1980!

On nonparametric Bayesian modeling and inference, including various versions of "Chinese restaurant process priors" that allow flexible structures to be learned and that allow sharing of statistical strength among sets of related structures.

Python module for Markov chain Monte Carlo sampling (Metropolis-Hastings).

A generalization of the Bayesian theory of subjective probability which does not require exact probabilities for each question of interest; can yield answers which contradict those arrived at using probability theory.

Presents a series of objections to Bayesian inference, written in the voice of a hypothetical anti-Bayesian statistician.

First steps towards automated rationality training that fixes common biases in judgment.

Universal prediction, i.e. Bayesian framework + Solomonoff prior + model class of all computable sequences, solves/avoids foundational problems of inductive inference, but has to be compromised in practice.

Friston's "brains are Bayesian predictors that minimize (thermodynamic) free energy" idea goes mainstream.

Convinced me that Bayesian inference over hierarchies of flexibly structured representations is a good model of human inductive learning.

"The bulk of human knowledge is organized around causal, not probabilistic relationships, and the grammar of probability calculus is insufficient for capturing those relationships."

"Our results suggest that everyday cognitive judgments follow the same optimal statistical principles as perception and memory, and reveal a close correspondence between people’s implicit probabilistic models and the statistics of the world."

Not every change is an improvement, but every improvement is necessarily a change. If we only admit small local errors, we will only make small local changes. We could move so much faster.

The two envelopes problem is a puzzle or paradox within the subjectivistic interpretation of probability theory; more specifically within Bayesian decision theory. This is still an open problem among the subjectivists as no consensus has been reached yet.

One of the justifications for the use of Bayesian probability theory. Probability is interpreted as a formal system of logic, the natural extension of Aristotelian logic into the realm of reasoning in the presence of uncertainty.

A naive Bayes classifier (also known as Idiot's Bayes) is a simple probabilistic classifier based on applying Bayes' theorem with strong (naive) independence assumptions.

Bayesian inference is statistical inference in which evidence or observations are used to update or to newly infer the probability that a hypothesis may be true.