Titlebook: Complex Data Modeling and Computationally Intensive Statistical Methods; Pietro Mantovan,Piercesare Secchi Book 2010 Springer-Verlag Milan

mortuary

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A parametric Markov chain to model age- and state-dependent wear processes,to the literature, it results that statisticians and engineers have almost always modeled wear processes by . increments models, which imply that future wear is assumed to depend, at most, on the system’s age. In many cases itseems to be more realistic and appropriate to adopts to chastic models whi

拱墻

Case studies in Bayesian computation using INLA,model, for instance, time and space dependence or the smooth effect of covariates. Many well-known statistical models, such as smoothing-spline models, space time models, semiparametric regression, spatial and spatio-temporal models, log-Gaussian Cox models, and geostatistical models are latent Gaus

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A graphical models approach for comparing gene sets,at are, somehow, functionally related. For example, genes appearing in a known biological pathway naturally define a gene set. Gene sets are usually identified from a priori biological knowledge. Nowadays, many bioinformatics resources store such kind of knowledge (see, for example, the Kyoto Encycl

和諧

Predictive densities and prediction limits based on predictive likelihoods,vation and the model parameter. Since, according to the likelihood principle, all the evidence is contained in the joint likelihood function, a predictive likelihood for the future observation is obtained by eliminating the nuisance quantity, namely the unknown model parameter. This paper focuses on

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acrobat

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