Nature

Bayesian Predictive Density Estimation

Bayesian predictive density estimation represents a cornerstone of modern statistical inference by integrating prior knowledge with observed data to produce a predictive distribution for future ...
Applied Clinical Trials Online

How Bayesian Approaches Can Revolutionize Rare Disease Clinical Trials

As rare disease trials face persistent feasibility challenges, Bayesian designs are gaining momentum by enabling more ...
JSTOR Daily

Multivariate Fay–Herriot Bayesian estimation of small area means under functional measurement error

Journal of the Royal Statistical Society. Series A (Statistics in Society), Vol. 180, No. 4 (OCTOBER 2017), pp. 1191-1209 (19 pages) Area level models, such as the Fay–Herriot model, aim to improve ...
Risk

Bayesian nonparametric covariance estimation with noisy and nonsynchronous asset prices

The covariance matrix of asset returns is the key input for many problems in finance and economics. This paper introduces a Bayesian nonparametric method to estimate the ex post covariance matrix from ...
JSTOR Daily

NETWORK AUTOCORRELATION MODELING: BAYESIAN TECHNIQUES FOR ESTIMATING AND TESTING MULTIPLE NETWORK AUTOCORRELATIONS

The network autocorrelation model has been the workhorse for estimating and testing the strength of theories of social influence in a network. In many network studies, different types of social ...
CU Boulder News & Events

Stats, Optimization, and Machine Learning Seminar - Nishant Mehta

I will present new excess risk bounds for randomized and deterministic estimators, discarding boundedness assumptions to handle general unbounded loss functions like log loss and squared loss under ...
lse

Bayesian Machine Learning

This course is available on the MSc in Applied Social Data Science, MSc in Data Science, MSc in Econometrics and Mathematical Economics, MSc in Health Data Science, MSc in Operations Research & ...