Relative model selection via Reversible Jump
Reversible Jump PDMP Samplers for Variable Selection
... for model choice. Motivated by variable selection problems, we show how to develop reversible jump PDMP sam- plers that can jointly explore the discrete space of models and the continuous ...
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Bayesian multi-locus pattern selection and computation through reversible jump MCMC
... In the human genome, susceptibility to common diseases is likely to be determined by interactions between multiple genetic variants. We propose an innovative Bayesian method to tackle the challenging problem of ...
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Bayesian Parameter Estimation and Model Selection of a Nonlinear Dynamical System using Reversible Jump Markov Chain Monte Carlo
... the Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm when applied to system identification problems which involve both parameter estima- tion and model ...a model. It is often the ...
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Data-Driven Reversible Jump for QTL Mapping
... Table 2 shows a posteriori probabilities for K calculated as the relative frequency of each value of K in the sequence. The highest a posteriori probability estimate for each situation is in boldface type and the ...
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Reversible Jump Markov Chain Monte Carlo
... the model at the posterior mode of the ARMA order and parameter space against the pointwise posteriors (mode and 80% credible set) over all impulse responses weighted by posterior ...driven selection of the ...
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A test of relative similarity for model selection in generative models
... the Relative MMD test can provide an automatic significance value without expensive cross-validation ...of relative tests with shrink- ing bandwidth are unknown: even for two samples this is challenging ...
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Model Selection of RBF Networks via Genetic Algorithms
... a selection method, such as the roulette wheel, the architectures are selected according to a relative probability associated to their ...The selection continues until the next population of ...
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Bayesian Volterra system identification using reversible jump MCMC algorithm
... Volterra model has been used for parametric loudspeaker system identifi- cation in [4] and for acoustic echo cancellation in [5] ...estimated via adaptive algorithms [6] ...nonlinear model is of ...
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Crisis, Value at Risk and Conditional Extreme Value Theory via the NIG + Jump Model
... the relative in-sample performance of the five models in one-day VaR ...best model for VaR measurement should give the exact number of expected exceedences, the EVT-NIG + Jump model ...
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Reversible jump Markov chain Monte Carlo method for parameter reduction in claims reserving
... which model, M k , it prefers (is the most likely for the particular data ...the reversible jump Markov chain Monte Carlo (RJMCMC) method which is a particular Markov chain Monte Carlo (MCMC) method ...
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On the Application of the Reversible Jump Markov Chain Monte Carlo Method within Structural Dynamics
... of model updating with both deterministic and non-deterministic updating methods having been extensively ...the model selection ...damage. Reversible Jump Markov Chain Monte Carlo ...
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Rejoinder: Latent variable graphical model selection via convex optimization
... graphical model selection in the Gaussian ...perform model selection? We noted that if the number of latent variables h is small relative to p and if the condi- tional statistics of the ...
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Discriminative Feature Selection via Multiclass Variable Memory Markov Model
... A selection criterion, similar to the one we propose here, was suggested by Goodman and Smyth for decision tree de- sign ...feature selection, Goodman and Smyth noted that with the assumption that all ...
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Bayesian System Identification of Dynamical Systems using Reversible Jump Markov Chain Monte Carlo
... of Reversible Jump Markov Chain Monte Carlo (RJMCMC) methods for nonlinear system ...and model selection to be addressed ...to jump between parameter spaces of varying ...
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Marginal reversible jump Markov chain Monte Carlo with application to motor unit number estimation
... variables via an impor- tance sampling ...that model choice criteria (as opposed to posterior model probabilities) such as DIC (Spiegelhalter et ...cross model posterior probabilities and we ...
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Model Selection via the VC Dimension
... complicated bootstrap procedure that would otherwise be sufficient. Thus, to implement our method here, we perform a restricted bootstrap. Specifically, we bootstrap in each level of the design variable (incomplete ...
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Berman-Konsowa principle for reversible Markov jump processes
... JUMP PROCESSES FRANK DEN HOLLANDER AND SABINE JANSEN Abstract. In this paper we prove a version of the Berman-Konsowa principle for reversible Markov jump processes on Polish spaces. The ...
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Berman-Konsowa principle for reversible Markov jump processes
... arXiv:1309.1305v2 [math.PR] 20 Oct 2016 JUMP PROCESSES FRANK DEN HOLLANDER AND SABINE JANSEN Abstract. In this paper we prove a version of the Berman-Konsowa principle for reversible Markov jump ...
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Improved Reversible Jump Algorithms for Bayesian Species Delimitation
... each model are highly concentrated. Proposed between-model jumps tend to be rejected, so that the chain becomes trapped in one model, even if that model has low posterior ...P jump 1, ...
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Reversible jump MCMC for nonparametric drift estimation for diffusion processes
... incorporating reversible jump MCMC steps in our computational algorithm, we will show that it can indeed lead to a considerably faster procedure compared to truncating at some fixed high ...
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