Bayesian statistics

The same points can be made more formally using Bayesian statistics.

He was known for his work on Bayesian statistics.

He has also worked in the field of Bayesian statistics, particularly with astronomical applications.

Some observers felt that it should have been possible to use classical Bayesian statistics.

The above definition is phrased in the context of Bayesian statistics.

Bayesian statistics are based on a different philosophical approach for proof of inference.

He frequently writes about Bayesian statistics, displaying data, and interesting trends in social science.

In the context of Bayesian statistics, it may also be referred to as the evidence or model evidence.

Many aspects of human perceptual and motor behavior can be modeled with Bayesian statistics.

Bayesian statistics is inherently sequential and so there is no such distinction.

He has also advocated Bayesian statistics, arguing for its power in formulating and evaluating economic policies.

Exponential families are also important in Bayesian statistics.

Quasi-likelihood has no role in Bayesian statistics, as this is based on a fully specified probability model for the data.

In Bayesian statistics, the term a priori denotes knowledge about the prior distribution.

This allows us to treat as a random variable as in Bayesian statistics.

The gamma distribution is widely used as a conjugate prior in Bayesian statistics.

In Bayesian statistics, hypothesis testing of the type used in classical power analysis is not done.

He is a leading proponent of Bayesian statistics.

Bayesian statistics is often used for inferring latent variables.

Exponential families have conjugate priors, an important property in Bayesian statistics.

After his death, his paper was exhumed, and the field of Bayesian statistics was born.

In statistics, and especially Bayesian statistics, the theorem is usually applied to real functions.

Gamma-minimax decision rules are of interest in robustness studies in Bayesian statistics.

This field of study has its historical roots in numerous disciplines including machine learning, experimental psychology and Bayesian statistics.

In Bayesian statistics, one would instead use a Dirichlet distribution as conjugate prior.