BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Finn Lindgren (University of Edinburgh) DTSTART:20230314T121500Z DTEND:20230314T130000Z DTSTAMP:20260422T155447Z UID:gbgstats/17 DESCRIPTION:Title: Stochastic adventures in space and time\nby Finn Lindgren (Universit y of Edinburgh) as part of Gothenburg statistics seminar\n\nLecture held i n MVL14.\n\nAbstract\nThe standard geostatistics toolbox includes methods for modelling\nspatial dependence between georeferenced observations\, as well as\nmethods for modelling the occurrence of random points. The core\ nmodel building blocks are often some form of Gaussian random fields.\n\nT he easiest approach to constructing space-time models is by taking\nthe pr oduct between a spatial covariance kernel and a temporal\ncovariance kerne l. These are called covariance separable models. An\nalternative that may better capture the spatio-temporal dynamics is to\ntake inspiration for ph ysics motivated partial differential equations\nsuch as the heat equation\ , which leads to non-separable models.\nNon-separable models are in genera l more computationally expensive\,\nbut one can sometimes use the model st ructure to retain a lot of the\nsimplicity of separable models\, for examp le allowing these models to\nbe used as components of larger hierarchical generalised additive\nmodels. For point process observations\, such as obs ervations of a\nmoving animal\, the temporal dynamics poses an additional challenge.\n\nI will discuss some of these aspects\, including a basic con struction\nof non-separable space-time models\, as well as an application of the\nINLA/inlabru framework to estimate the parameters of a dynamical\n animal movement model by rephrasing it as a point process model\, with\na parametric movement kernel\, and a random field as an unknown\n"resource s election function".\n LOCATION:https://researchseminars.org/talk/gbgstats/17/ END:VEVENT END:VCALENDAR