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Generalized Linear Models have become a commonly used tool of data analysis. Such models are used to fit regressions for univariate responses with normal, gamma, binomial or Poisson distribution. Maximum likelihood is generally applied as fitting method. I ...
We aim at the elaboration of Information Systems able to optimize energy consumption in buildings while preserving human comfort. Our focus is in the use of state-based stochastic modeling applied to temporal signals acquired from heterogeneous sources suc ...
EPFL Solar Energy and Building Physics Laboratory (LESO-PB)2013
In this paper we explore the impact of several sources of uncertainties on the assessment of energy and climate policies when one uses in an harmonized way stochastic programming (SP) in a large scale bottom-up (BU) model and Monte-Carlo simulation (MC) in ...
Model specification is an integral part of any statistical inference problem. Several model selection techniques have been developed in order to determine which model is the best one among a list of possible candidates. Another way to deal with this questi ...
We propose a novel stochastic optimization algorithm, hybrid simulated annealing (SA), to train hidden Markov models (HMMs) for visual speech recognition. In our algorithm, SA is combined with a local optimization operator that substitutes a better solutio ...
An important problem in logistic regression modeling is the existence of the maximum likelihood estimators. In particular, when the sample size is small, the maximum likelihood estimator of the regression parameters does not exist if the data are completel ...
For good performance in practice, real-time optimization schemes need to be able to deal with the inevitable model-mismatch problem. Unlike the two-step schemes combining parameter estimation and optimization, the modifier-adaptation approach uses experime ...
In conventional vector space model for information retrieval, query vector generation is imperfect for retrieval of precise documents which are de-sired by user. In this paper, we present a stochastic based approach for optimiz-ing query vector without use ...
In this thesis, we focus on standard classes of problems in numerical optimization: unconstrained nonlinear optimization as well as systems of nonlinear equations. More precisely, we consider two types of unconstrained nonlinear optimization problems. On t ...
Call centre scheduling aims to determine the workforce so as to meet target service levels. The service level depends on the mean rate of arrival calls, which fluctuates during the day, and from day to day. The staff schedule must adjust the workforce peri ...