The Bayesian approach to machine learning amounts to inferring posterior distributions of
random variables from a probabilistic model of how the variables are related (that is, a prior …

Sufficiency has long been regarded as the primary reduction procedure to simplify a
statistical model, and the assessment of the procedure involves an implicit global repeated …

The Bayesian approach to machine learning amounts to computing posterior distributions of
random variables from a probabilistic model of how the variables are related (that is, a prior …

We show that the disintegration operator on a complete separable metric space along a
projection map, restricted to measures for which there is a unique continuous disintegration …

The likelihood statistic guides statistical analysis in almost all areas of application. A precise
definition of likelihood statistic is given, and simple and easy to use criteria are proposed to …

We use the minimal sufficiency of the likelihood map (Fraser et al.[9]) to show the existence
of a minimal sufficient statistic with Euclidean range under weak regularity conditions, and to …

A Minimal Sufficient Statistic and Representations of the Densities Page 1 ? Board of the
Foundation of the Scandinavian Journal of Statistics 1996. Published by Blackwell Publishers …

Bulan Desember dan Januari, masa di mana hujan sedang deras-derasnya turun di
Indonesia. Hampir semua orang mempersiapkan diri dengan membeli dan memakai …

The definition of conditional probability is discussed in detail. The definition is based on the
derivative of measures and covers conditioning with respect to events of probability zero. It …

We use the minimal sufficiency of the likelihood map (Fraser et al.[9]) to show the existence
of a minimal sufficient statistic with Euclidean range under weak regularity conditions, and to …