The main focus of this short course will be the Bayesian aspect of it. That means this is a slightly more advanced course requiring some knowledge of basic probability, regression methods, and the R ...

We discuss two methods of making nonparametric Bayesian inference on probability measures subject to a partial stochastic ordering. The first method involves a nonparametric prior for a measure on ...

Bayesian methods have emerged as a pivotal framework in the design and analysis of clinical trials, offering a systematic approach for updating evidence as new data become available. By utilising ...

Bayesian inference has been used for genetic risk calculation. In this traditional method, inheritance events are divided into a number of cases under the inheritance model, and some elements of the ...

Every utility matrix in a Bayesian decision problem determines a "critical probability" for each of the states of nature: when a critical probability is exceeded, the assessment of the remaining ...

Scientists have developed a method to identify symmetries in multi-dimensional data using Bayesian statistical techniques. Bayesian statistics has been in the spotlight in recent years due to ...

We live in a world where a lot of things seem to happen by pure chance, from winning the Lotto to losing your car keys. But the truth is, the likelihood of many everyday things happening is heavily ...

Symmetries in nature make things beautiful; symmetries in data make data handling efficient. However, the complexity of identifying such patterns in data has always bedeviled researchers. Scientists ...