Aguêmon Yves Atchadé
I'm Associate Professor of Statistics at the
University of Michigan
Department of Statistics.
My current research interests revolve around computational methods in
statistics, with an emphasis on high-dimensional problems.
- A central theme of my research are Monte Carlo
methods. My current research explores the possibilities and limits of
Markov Chain Monte Carlo methods in dealing with posterior or
quasi-posterior distributions that arise from high-dimensional Bayesian
(or quasi-Bayesian) inference in regression and graphical models. In
optimization, my current research revolves around the use of stochastic
methods in optimization, and whether (and how) this can help tackle
large scale statistical problems.
- I also have a growing interest in the use of remote sensing data
to study social and environmental issues in Africa.
- I have a postdoc opening on high-dimensional Bayesian asymptotics and computation. Email me if interested.
New Technical Reports
- Efficiency bounds for semiparametric models with singular score
functions (file in pdf here). Joint
work with Prosper
- A Scalable quasi-Bayesian framework for Gaussian graphical
models. (File in pdf)
- On the contraction properties of some high-dimensional
quasi-posterior distributions. (File in pdf).
- Scalable Computation of Regularized Precision Matrices via
Stochastic Optimization. (File in pdf).
Joint with Rahul
Mazumder, and Jie Chen.
- A Moreau-Yosida approximation for high-dimensional posterior and
quasi-posterior distributions. (File in pdf). There is a supplementary file.