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Diagnostics for Gaussian Process EmulatorsLeonardo S. Bastos ∗University of SheffieldAnthony O’HaganUniversity of SheffieldMathematical models, usually implemented in computer programs known as simulators, are widelyused in all areas of science and technology to represent complex real-world phenomena. Simulatorsare often so complex that they take appreciable amounts of computer time or other resources to run.In this context, a methodology has been developed based on building a statistical representation of thesimulator, known as an emulator. The principal approach to building emulators uses Gaussian processes.This work presents some diagnostics to validate and assess the adequacy of a Gaussian process emulatoras surrogate for the simulator. These diagnostics are based on comparisons between simulator outputsand Gaussian process emulator outputs for some test data, known as validation data, defined by a sampleof simulator runs not used to build the emulator. Our diagnostics take care to account for correlationbetween the validation data. To illustrate a validation procedure, we apply these diagnostics to twodifferent data sets.∗ Apresentador/Speaker27
Bayesian Coalescent Modeling of the Evolutionary Response ofHIV to TherapyAlexander F. BraunsteinDepartment of Statistics, The Wharton School,University of PennsylvaniaShane T. Jensen ∗Department of Statistics, The Wharton School,University of PennsylvaniaJon McAuliffeDepartment of Statistics, The Wharton School,University of PennsylvaniaStatistical evolutionary models provide an important mechanism for describing and understandingthe escape response of a viral population under a particular therapy. We present a coalescent-basedmodel that incorporates spatially varying mutation and recombination rates at the nucleotide level. Ourhierarchical structure also maintains separate parameters for treatment and control groups, which allowsus to estimate treatment effects explicitly. We use the model to investigate the sequence evolution of HIVpopulations exposed to a recently developed antisense gene therapy, as well as a more conventional drugtherapy. The detection of biologically relevant and plausible signals in both therapy studies demonstratesthe effectiveness of the method.∗ Apresentador/Speaker28
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Page 9 and 10: Particle LearningCarlos M. Carvalho
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Bayesian Inference for Gamma Modula
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Computational Tools for Comparing A
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Modelo Partição Produto com “Cl
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BIC Criterion for Minimal Markov Mo
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Estimadores Lineares Bayesianos em
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Modelos de Volatilidade Estocástic
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Processo de Difusão Multivariado c
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Generalized Latent Factor Models fo
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Measuring Vulnerability via Spatial
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Aproximações Analíticas para Pos
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Modelo de Risco Logístico Dependen
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A Practical Approach to Elicit Mult
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Modelos Dinâmicos para Deformaçã
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A Semiparametric Bayesian Approach
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Modelo Weibull Modificado de Longa
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Abordagem Bayesiana para Modelos GA
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Modelo Bayesiano Geral de Classe La
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Métodos de Estimação em Modelos
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LASSO Bayesiano no Mapeamento de QT
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Estudo das Matrizes de Covariância
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Sensibilidade da Inferência Bayesi
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Análise Bayesiana para o Estudo de
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Uma Abordagem Bayesiana para Modelo
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Construção de Tábuas de Mortalid
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Um Procedimento Bayesiano para a An
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Um Novo Modelo para Dependência Es
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Distribuição Geométrica Exponenc
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Morbidade Pulmonar e Condições Cl
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Standard Setting for a Rasch Poisso
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Modelo Logístico Misto com Classes
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Metodologia Bayesiana para Detecç
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Modelos Espaço-Temporais para Dado
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Comparação de Modelos para a Iden
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Modelos Multivariados de Regressão
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Estimação Bayesiana em Modelos de
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Previsão de Resultados de Jogos de
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Uma Abordagem Bayesiana para Modelo
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Análise Bayesiana do Modelo Log-Bi
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Desempate TécnicoFilipe Jaeger Zab
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10 o Encontro Brasileiro de Estatí
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