Bayesian Inference for Partially Observed IBD
Bayesian inference and model selection for partially observed stochastic epidemics
... Statistical inference and model selection for stochastic epi- demic models So far, we have focused on reviewing some of the existing work on modelling the dynamics of an infectious ...drawing inference ...
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Bayesian and Frequentist Inference in Partially Identified Models
... of partially identified structural parameters is derived for models that can be indexed by a finite-dimensional reduced form parameter ...and Bayesian credible sets in partially identified ...whereas ...
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Efficient Bayesian inference for partially observed stochastic epidemics and a new class of semi parametric time series models
... and inference needs to be drawn for both of ...the observed data contain more information about the mean infectious period, rather than the parameters α and γ ...
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Local robustness of Bayesian parametric inference and observed likelihoods
... given observed likelihood, even when the sample distribution family is not accurately speci…ed and the data is not a random ...a Bayesian might plausibly assert that her functioning and genuine prior would ...
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Control and surveillance of partially observed stochastic epidemics in a Bayesian framework
... In the final part of this thesis, we develop a mathematical model that can pro- vide insight into likelihood of spread between countries of the disease Peste des Petits Ruminants (PPR) and risk of introduction across ...
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Consistency of Bayesian nonparametric inference for discretely observed jump diffusions
... of Bayesian nonparametric inference for jump diffusions with unit diffusion coefficient and uniformly Lipschitz drift and jump coefficients in ar- bitrary ...
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Bayesian inference for indirectly observed stochastic processes, applications to epidemic modelling
... In addition, we have shown on two simulated datasets that updating simultaneously the paths and parameters of the system with the advanced HMC algorithm could lead to better performance than a simple adaptive ...
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Bayesian model based spatiotemporal survey designs and partially observed log Gaussian Cox process
... Our results show that in the presence of prior information on intensity function, survey designs that are expected to be most informative for partially observed LGCPs are different from traditional designs ...
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Evaluation of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems
... only partially observed, which makes statistical inference in those systems ...The inference prob- lem for stochastic dynamical systems usually includes both state and parameter ...
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Efficient Bayesian model choice for partially observed processes : with application to an experimental transmission study of an infectious disease
... to observed data. Performing robust inference for these systems is ...of inference techniques that are able to numerically integrate over multiple hidden states and/or infer missing ...Approximate ...
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Bayesian variable selection using partially observed categorical prior information in fine-mapping association studies
... in Bayesian fine-mapping case-control association ...of partially-observed functional genomic ...formal Bayesian variable selection approach and either limit the number of causal SNPs allowed ...
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Predictive Classification and Bayesian Inference
... direct inference about ob- servables would better serve the purpose of statistical modelling in many different ...conventional Bayesian approach models the distribution of data based on parameters, which are ...
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Bayesian Inference on Gravitational Waves
... every observed frequency in a DFTed data ( ̃ ) there is a corresponding frequency of parameter component ̃ ) as well as of a power spectrum component ( ) and the summation now runs over frequencies rather than ...
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Bayesian Nonparametric and Parametric Inference
... Parametric Inference It seems to me that there is a contradiction at the heart of Bayesian parametric ...been observed, some sort of check is made to see whether the data is com- patible with the ...
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Bayesian inference via projections
... Abstract Bayesian inference often poses difficult com- putational ...the observed variables; ...tribution. Inference is performed by sampling from the posterior implied by the first component, ...
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Bayesian inference via projections
... Abstract Bayesian inference often poses difficult com- putational ...the observed variables; ...tribution. Inference is performed by sampling from the posterior implied by the first component, ...
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Computational Neuropsychology and Bayesian Inference
... a Bayesian framework is that it captures many different types of behavior, including apparently suboptimal ...an observed behavior Bayes ...implement Bayesian inference ( Schwartenbeck et ...
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Neural Plausibility of Bayesian Inference
... performing Bayesian inference by combining the learned prior with the likelihood that is based on the observed ...perform inference using those ...
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Piecewise Approximate Bayesian Computation: fast inference for discretely observed Markov models using a factorised posterior distribution
... S.R. White · T. Kypraios · S.P. Preston Received: 17 December 2012 / Accepted: 24 October 2013 / Published online: 29 November 2013 © The Author(s) 2013. This article is published with open access at Springerlink.com ...
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Piecewise Approximate Bayesian Computation: fast inference for discretely observed Markov models using a factorised posterior distribution
... The stochastic Lotka–Volterra (LV) model is a model of predator–prey dynamics and an example of a stochastic discrete-state-space continuous-time Markov process (see, for example, Wilkinson 2011a). Predator–prey dynamics ...
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