Bayes-Raking: Bayesian Finite Population Inference with Known Margins

Bayesian approach to inference of population structure

Methods of inferring the population structure‎, ‎its applications in identifying disease models as well as foresighting the physical and mental situation of human beings have been finding ever-increasing importance‎. ‎In this article‎, ‎first‎, ‎motivation and significance of studying the problem of population structure is explained‎. ‎In the next section‎, ‎the applications of inference of p...

Bayesian inference for finite population quantiles from unequal probability samples.

This paper develops two Bayesian methods for inference about finite population quantiles of continuous survey variables from unequal probability sampling. The first method estimates cumulative distribution functions of the continuous survey variable by fitting a number of probit penalized spline regression models on the inclusion probabilities. The finite population quantiles are then obtained ...

Kernel Bayes' rule: Bayesian inference with positive definite kernels

A kernel method for realizing Bayes’ rule is proposed, based on representations of probabilities in reproducing kernel Hilbert spaces. Probabilities are uniquely characterized by the mean of the canonical map to the RKHS. The prior and conditional probabilities are expressed in terms of RKHS functions of an empirical sample: no explicit parametric model is needed for these quantities. The poste...

PAC-Bayes & Margins

Bayesian Inference with Efficient Neural Population Codes

The accuracy with which the brain can infer the value of a stimulus variable depends on both the amount of stimulus information that is represented in sensory neurons (encoding) and the mechanism by which this information is subsequently retrieved from the responses of these neurons (decoding). Previous studies have mainly focused on either the encoding or the decoding aspect. Here, we present ...