Conveners
INVITED SFdS on Bayesian Statistics
- Jean-Michel Poggi (University of Paris-Saclay)
The Poisson log-normal (PLN) model is a generic model for the joint distribution of count data, accounting for covariates. It is also an incomplete data model. A classical way to achieve maximum likelihood inference for model parameters $\theta$ is to resort to the EM algorithm, which aims at maximizing, with respect to $\theta$, the conditional expectation, given the observed data $Y$, of the...
When computer codes are used for modeling complex physical systems, their unknown parameters are tuned by calibration techniques. A discrepancy function is added to the computer code in order to capture its discrepancy with the real physical process. This discrepancy is usually modeled by a Gaussian process. In this work, we investigate a Bayesian model selection technique to validate the...
We explore several statistical learning methods to predict individual electrical load curves using customers’ billing information. We predict the load curves by searching in a catalog of available load curves. We develop three different strategies to achieve our purpose. The first methodology relies on estimating the regression function between the load curves and the predictors (customers’...