Il est bien connu qu’une chaîne de Markov finie apériodique irréductible converge vers sa distribution stationnaire. Mais à quelle vitesse ? Il s’agit là souvent d’une question difficile. Dans cet exposé je présenterai une famille de chaînes de Markov, appelées chaînes montantes-descendantes, pour lesquelles on peut calculer de manière exacte la distance (au sens de la distance de séparation) entre la chaîne de Markov et sa distribution stationnaire. Un exemple dans le monde des permutations sera présenté et étudié plus spécifiquement.

The increasing availability of temporal and geo-coded data underscores the importance of spatio-temporal statistical modelling in tackling complex issues across various real-world settings. In the first part of this talk, we will briefly showcase novel spatio-temporal statistical methods developed to model various types of data defined both in space and time (e.g., time-series, point patterns, lattice data, geostatistical data, etc.), with a focus on applications in environmental and public health domains. In the second part, we will (I) delve into the modelling of trajectory (or path) data and (II) explore the details of a statistical method for addressing spatially varying preferential sampling when modelling geostatistical data. Specifically, we will account for preferential sampling by including a spatially varying coefficient that describes the dependence strength between the process that models the sampling locations and the corresponding latent field. We achieve this by approximating the preferentiality component with a set of basis functions, with the corresponding coefficients estimated using the integrated nested Laplace approximation (INLA) method. This approach allows for efficient inference with a low computation burden.

L’orateur du seminaire donnera un expose’ introductif.