We propose a nonparametric estimation theory for the occupation density, the drift vector, and the diffusion matrix of multivariate diffusion processes. The estimators are sample analogues to ...

We consider two new models of reducible age-dependent branching processes with emigration in conjunction with estimation problems arising in cell biology. Methods of statistical inference are ...

Robust estimation and risk minimisation in stochastic processes represent a central domain in modern statistical research. These methods are designed to ensure that statistical predictions remain ...

Entropy estimation and information theory form the bedrock of our understanding of uncertainty and complexity in both natural and engineered systems. At its core, entropy quantifies the ...

The identities or bounds that relate information measures (e.g., the entropy and mutual information) and estimation measures (e.g., the minimum means square error ...

CATALOG DESCRIPTION: Advanced topics in random processes: point processes, Wiener processes; Markov processes, spectral representation, series expansion of random processes, linear filtering, Wiener ...

Distributed inference when the participants are only machines or electronic devices, e.g., sensors, has been explored extensively in the signal processing and machine learning literature. However, ...

This post was co-authored with Professor John Vervaeke 1. Thinking, Fast and Slow is one of the great books written on cognition and decision making. In a clear and accessible fashion, it made popular ...