[ABE-L] ERRATA: Seminário DEST/UFMG em 31/03/2023

[Marcos Prates]

Caros,

o seminário do Jorge Mateu ocorrerá no dia 31 de março pelo canal do
YouTube e não julho como no e-mail.

--marcos


On Fri, Mar 24, 2023, 15:00 Marcos Prates <marcosop em gmail.com> wrote:

> Caros,
>
> Na próxima sexta-feira (31 de julho, às 13:30h) o ciclo de Seminários do
> Departamento de Estatística da UFMG terá a apresentação do prof. Jorge
> Mateu.
>
> O Prof. Jorge é professor Titular do departamento de Matemática da
> Universitat Jaume I de Castellón. Possui graduação, mestrado e doutorado em
> Estatística pela Universitat de Valencia. Suas áreas de pesquisa são:
> Estatística Espacial e Processos Pontuais Espaço Temporal.
>
> Título: Statistical models for the analysis, prediction and monitoring of
> space-time data. Applications to infectious diseases and crime
>
> Resumo:
> We present several statistical approaches to understand the underlying
> temporal and spatial dynamics of infectious diseases (with a focus on
> Covid-19 data) that can result in informed and timely public health
> policies. Most studies in the context of infectious diseases commonly
> report figures of the overall infection at a state- or county-level,
> reporting the aggregated number of cases in a particular region at one
> time. However, we focus on analysing high-resolution Covid-19 datasets in
> form of spatio-temporal point patterns, offering vital insights for the
> spatio-temporal interaction between individuals concerning the disease
> spread in a metropolis.
>
> We develop a non-stationary spatio-temporal point process, assuming that
> previously infected cases trigger newly confirmed ones, and introduce
> a neural network-based kernel to capture the spatially varying triggering
> effect. The neural network-based kernel is carefully crafted to
> enhance expressiveness while maintaining results interpretability. We also
> incorporate some exogenous influences imposed by city landmarks.
> Additionally, we propose some mechanistic models giving particular
> data-driven forms to the spatio-temporal intensity function. Particular
> cluster spatio-temporal models to identify unknown parents are also
> depicted. For  completeness, we present a method to evaluate the direction
> and velocities of the spread
> by considering the intensity comes from a growth differential equation.
>
> Crime science deals with the analysis of crime data from many
> perspectives. This type of data brings up a large variety of problems
> linked with data science and big data analysis. In general grounds, crime
> data provides heterogeneous patterns in space and time, and we present
> methods able to handle this heterogeneity. In particular, we consider
> statistical models to detect generators of crime in cities together with
> potential focuses that attract or inhibit crimes in a spatio-temporal
> region. We also consider methods to reduce potential large dimensionality
> in the data, and some artificial intelligent methods to help handling large
> amounts of crime data. Two final crucial probabilistic models will be
> presented. One is modelling crime data using stochastic point pattern
> processes, such as log-Gaussian Cox processes, that will be used to
> forecast and predict risk of crimes in subregions of space and time of a
> city. This aspect will be complemented with another type of stochastic
> models with differential.
>
> O seminário será transmitido ao vivo pelo canal do Youtube "Seminários
> DEST - UFMG <https://www.youtube.com/@seminariosdest-ufmg>".
>
> https://www.youtube.com/@seminariosdest-ufmg
>
> Att,
> Marcos Prates
>
-------------- Próxima Parte ----------
Um anexo em HTML foi limpo...
URL: <http://lists.ime.usp.br/pipermail/abe/attachments/20230326/c03d7f25/attachment.htm>