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About me. I am a Professor of Data Sciences/Statistics/Machine Learning at ENSAE, Institut Polytechnique de Paris. I am particularly interested in all aspects of Bayesian computation, that is algorithms to perform Bayesian inference, including: fast approximations: e.g. Expectation Propagation and variational Bayes. On this site you can find ...
- ENSAE, Paris
Nicolas Chopin was born in the village of Marainville-sur-Madon ( Vosges department ), in the province of Lorraine, France. He was the son of François Chopin (9 November 1738, Ambacourt – 31 January 1814, Marainville), a wheelwright and village administrator for Marainville, and Marguerite, née Deflin (1 February 1736, Diarville – 21 ...
- 15 April 1771, Marainville-sur-Madon, Vosges, France
Chopin, N., and Pelgrin, F. Bayesian inference and state number determination for hidden Markov models: an application to the information content of the yield curve about inflation. J. Econometrics 123 , 2 (2004), 327–344.
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Nicolas Chopin Professor of data sciences ENSAE 5 avenue Henry Le Chatelier 91764 Palaiseau CedexFRANCE — +33 1 41 17 65 22 # nicolas.chopin@ensae.fr ‡ https://nchopin.github.io/ Experience Sept. 2006– to date Professor of data sciences, ENSAE, IPP, Palaiseau, FRANCE 2003–2006 Lecturer in Statistics, Bristol University, UK
Nicolas Chopin. Professor of Statistics, ENSAE (Institut Polytechnique de Paris) Verified email at ensae.fr - Homepage. Statistics Bayesian Stastistics Bayesian Machine Learning Monte Carlo methods Bayesian computation particle filtering.
Nicolas Chopin In the context of state-space models, backward smoothing algorithms rely on a backward sampling step, which by default has a O(N^2) complexity (where N is the number of particles).
I am a professor of Statistics at the ENSAE since 2006. Before that I was a lecturer at the Department of Mathematics of Bristol University (UK). My main research interest is SMC (Sequential Monte Carlo, aka particle filtering), but I am also interested more generally in Monte Carlo, Bayesian Statistics, and Bayesian machine learning. New book ...
