Results 31 to 40 of about 3,465 (151)
Conservation Science and Practice, 2020 Endemic species face a variety of threats including predation from non‐native invaders. In some cases, however, invasive species can be managed by directly suppressing populations, and tracking technologies that allow researchers to identify movement ...
Lee F.G. Gutowsky +9 more
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Bayesian modeling of the temporal evolution of seismicity using the ETAS.inlabru package
Frontiers in Applied Mathematics and Statistics, 2023 The epidemic type aftershock sequence (ETAS) model is widely used to model seismic sequences and underpins operational earthquake forecasting (OEF).
Mark Naylor +4 more
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survHE: Survival Analysis for Health Economic Evaluation and Cost-Effectiveness Modeling
Journal of Statistical Software, 2020 Survival analysis features heavily as an important part of health economic evaluation, an increasingly important component of medical research. In this setting, it is important to estimate the mean time to the survival endpoint using limited information (
Gianluca Baio
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Comparison of inference methods of genetic parameters with an application to body weight in broilers [PDF]
Archives Animal Breeding, 2015 REML (restricted maximum likelihood) has become the standard method of variance component estimation in animal breeding. Inference in Bayesian animal models is typically based upon Markov chain Monte Carlo (MCMC) methods, which are generally flexible but
G. Maniatis +4 more
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Frontiers in Environmental Science, 2020 Due to the occurrence of more frequent and widespread toxic cyanobacteria events, the ability to predict freshwater cyanobacteria harmful algal blooms (cyanoHAB) is of critical importance for the management of drinking and recreational waters.
Mark H. Myer +3 more
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Spatial modelling with R-INLA: A review
, 2018 Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically-sized datasets from scratch is time-consuming, and if changes are made to the model, there is little guarantee that the code performs well.
Bakka, Haakon +7 more
openaire +2 more sources
Should you use data integration for your distribution model?
Journal of Animal Ecology, EarlyView.This paper explores cases where data integration (the joint modelling of two or more observational datasets) is useful for species distribution models, and also highlights cases where it's actually not useful. This provides the first concrete guidance for deciding whether or not data integration is worth your time.
Benjamin R. Goldstein +3 more
wiley +1 more source
Revista Latinoamericana de Población, 2019 El trabajo busca modelar la distribución de la tasa de incidencia acumulada de COVID-19 en los municipios de México a través del ajuste de tres modelos lineales generalizados (en competencia) con efectos espaciales y temporales y función de enlace ...
Gerardo Núñez Medina
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Modeling multivariate positive‐valued time series using R‐INLA
Applied Stochastic Models in Business and IndustryAbstractIn this article, we describe fast Bayesian statistical analysis of vector positive‐valued time series, with application to interesting financial data streams. We discuss a flexible level correlated model (LCM) framework for building hierarchical models for vector positive‐valued time series.
Dutta, Chiranjit +2 more
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A Bayes factor framework for unified parameter estimation and hypothesis testing
British Journal of Mathematical and Statistical Psychology, EarlyView.Abstract The Bayes factor, the data‐based updating factor of the prior to posterior odds of two hypotheses, is a natural measure of statistical evidence for one hypothesis over the other. We show how Bayes factors can also be used for parameter estimation.
Samuel Pawel
wiley +1 more source