Search results for "Bayesian model"

Italian Deprivation Index and Dental Caries in 12-Year-Old Children: A Multilevel Bayesian Analysis

2014

Evidence from the literature has shown that people with a lower socioeconomic status enjoy less good health than people with a higher socioeconomic status. The Italian deprivation index (DI) was used with the aim to evaluate the association between the DMFT index and risk factors for dental caries, including city population and DI. The study included 4,305 12-year-old children living in 38 cities classified by demographic size as small, midsize and large. Zero-inflated negative binomial multilevel regression models were used to assess risk factors for DMFT and to address excess of zero DMFT and overdispersion through a Bayesian approach. The difference in the average level of DMFT among chi…

Joint Estimation of Relative Risk for Dengue and Zika Infections, Colombia, 2015–2016

2019

We jointly estimated relative risk for dengue and Zika virus disease (Zika) in Colombia, establishing the spatial association between them at the department and city levels for October 2015–December 2016. Cases of dengue and Zika were allocated to the 87 municipalities of 1 department and the 293 census sections of 1 city in Colombia. We fitted 8 hierarchical Bayesian Poisson joint models of relative risk for dengue and Zika, including area- and disease-specific random effects accounting for several spatial patterns of disease risk (clustered or uncorrelated heterogeneity) within and between both diseases. Most of the dengue and Zika high-risk municipalities varied in their risk distributio…

Markov chain Monte Carlo importance samplers for Bayesian models with intractable likelihoods

2019

Markov chain Monte Carlo (MCMC) is an approach to parameter inference in Bayesian models that is based on computing ergodic averages formed from a Markov chain targeting the Bayesian posterior probability. We consider the efficient use of an approximation within the Markov chain, with subsequent importance sampling (IS) correction of the Markov chain inexact output, leading to asymptotically exact inference. We detail convergence and central limit theorems for the resulting MCMC-IS estimators. We also consider the case where the approximate Markov chain is pseudo-marginal, requiring unbiased estimators for its approximate marginal target. Convergence results with asymptotic variance formula…

Bayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues

2011

In this article, we describe the estimation of linear regression models with uncertainty about the choice of the explanatory variables. We introduce the Stata commands bma and wals, which implement, respectively, the exact Bayesian model-averaging estimator and the weighted-average least-squares estimator developed by Magnus, Powell, and Prüfer (2010, Journal of Econometrics 154: 139–153). Unlike standard pretest estimators that are based on some preliminary diagnostic test, these model-averaging estimators provide a coherent way of making inference on the regression parameters of interest by taking into account the uncertainty due to both the estimation and the model selection steps. Spec…

Small changes, big impacts: Geographic expansion in small-scale fisheries

2020

Abstract Small-scale fisheries are an important, yet neglected, millenarian activity that has been undergoing significant changes that threaten its future. Understanding how this activity is spatially distributed and the factors that drive its use of the marine space over time can shed some light on how fishing efforts and their impacts have moved over different parts of coastal marine ecosystems. This study investigated changes to the spatial distribution of small-scale fisheries along the Brazilian equatorial region between 1994 and 2014 and the factors, from ecological to socioeconomic, that influenced this shift. Bayesian hierarchical spatial models were used together with environmental…

Flexible Data Driven Inventory Management with Interactive Multiobjective Lot Size Optimization

2021

We study data-driven decision support and formalise a path from data to decision making. We focus on lot sizing in inventory management with stochastic demand and propose an interactive multi-objective optimisation approach. We forecast demand with a Bayesian model, which is based on sales data. After identifying relevant objectives relying on the demand model, we formulate an optimisation problem to determine lot sizes for multiple future time periods. Our approach combines different interactive multi-objective optimisation methods for finding the best balance among the objectives. For that, a decision maker with substance knowledge directs the solution process with one’s preference inform…

Evidence for spatiotemporal shift in demersal fishery management priority areas in the western Mediterranean

2022

14 pages, 10 figures, 2 tables, 1 appendix

Spatio-Temporal Modeling of Zika and Dengue Infections within Colombia

2018

The aim of this study is to estimate the parallel relative risk of Zika virus disease (ZVD) and dengue using spatio-temporal interaction effects models for one department and one city of Colombia during the 2015&ndash

Spatial Bayesian Modeling of Presence-only Data

2011

Interaction in Spoken Word Recognition Models: Feedback Helps

2018

Human perception, cognition, and action requires fast integration of bottom-up signals with top-down knowledge and context. A key theoretical perspective in cognitive science is the interactive activation hypothesis: forward and backward flow in bidirectionally connected neural networks allows humans and other biological systems to approximate optimal integration of bottom-up and top-down information under real-world constraints. An alternative view is that online feedback is neither necessary nor helpful; purely feed forward alternatives can be constructed for any feedback system, and online feedback could not improve processing and would preclude veridical perception. In the domain of spo…