[PDF] Top 20 Bayesian model comparison via sequential Monte Carlo
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Bayesian model comparison via sequential Monte Carlo
... Note that by “more data”, we not only refer the situation where a larger sample size is obtained, but also when the data are measured more accurately. For example, compare the results of using informative priors (Table ... See full document
241
Towards automatic model comparison : an adaptive sequential Monte Carlo approach
... for Bayesian model comparison has not yet received a great deal of ...posterior model probabilities using SMC, focusing on strategies that require minimal tuning and can be readily implemented ... See full document
27
On the use of Bayesian Monte-Carlo in evaluation of nuclear data
... One important point is that these methods could be used for resonance range analysis (both resolved and unresolved resonance) as well as higher energy models. In addition, both microscopic integral data assimilation ... See full document
6
Automatic kernel selection for Gaussian processes regression with approximate Bayesian computation and sequential Monte Carlo
... mechanism via Bayesian formulation of the ABC to find an optimal kernel and its hyperparameters ...kernel model, leaving one with the question is there a best model with best solution that ... See full document
13
Sparse Estimation in Ising Model via Penalized Monte Carlo Methods
... Summarizing, it is difficult to indicate the winner algorithm. We can observe that the quality of our algorithm in selecting the true model is satisfactory. Moreover, only this procedure works on a good level in ... See full document
26
Inversion of Schlumberger resistivity sounding data from the critically dynamic Koyna region using the Hybrid Monte Carlo-based neural network approach
... of Bayesian Neural Network ...present Bayesian Neural Net- work based resistivity model is well stable in presence of correlated red noise ... See full document
14
Bayesian analysis of chaos: The joint return volatility dynamical system
... novel Bayesian inference procedure for the Lyapunov exponent in the dynamical system of returns and their unobserved ...of Sequential Monte Carlo for Bayesian ...simpler model is ... See full document
20
Divide and conquer with sequential Monte Carlo
... The most well-known application of SMC is to the filtering problem in general state- space hidden Markov models, see e.g., Doucet and Johansen (2011) and references therein. However, these methods are much more generally ... See full document
30
Blind Decoding of Multiple Description Codes over OFDM Systems via Sequential Monte Carlo
... In this paper, we consider the problem of transmitting a continuous source through an OFDM system over parallel frequency-selective fading channels. The source signals are quantized and encoded by an MDSQ, resulting in ... See full document
14
A Bayesian Monte-Carlo Uncertainty Model for Assessment of Shear Stress Entropy
... entropy model in estimating the velocity distribution proposed by Moramarco et ...proposed model for estimating velocity in their study was satisfactory for calculating the average velocity in multiple ... See full document
47
Quasi Monte Carlo and multilevel Monte Carlo methods for computing posterior expectations in elliptic inverse problems
... a Bayesian posterior ...a model elliptic problem, we provide a full convergence and complexity analysis of the ratio estimator in the case where Monte Carlo, quasi-Monte Carlo or ... See full document
27
Stability of sequential Markov Chain Monte Carlo methods
... Abstract. Sequential Monte Carlo Samplers are a class of stochastic algorithms for Monte Carlo integral estimation ...chain Monte Carlo methods and importance ... See full document
10
Applying sequential Monte Carlo methods into a distributed hydrologic model: lagged particle filtering approach with regularization
... runoff model of Moradkhani et ...and model parameters, and Rings et ...hydrological model is an- alyzed by Salamon and Feyen (2009, 2010), and dual state- parameter updating of a conceptual ... See full document
15
Sparse Single-Index Model
... Langevin Monte Carlo and RJMCMC, ...single-index model, MCMC algorithms were used to compute Bayesian estimates by Antoniadis et ...fully Bayesian method to analyze the single-index ... See full document
38
Bayesian Mapping of Quantitative Trait Loci Under Complicated Mating Designs
... Mixed model: Assume that the mapping population to the need to invert large IBD matrices repeatedly for consists of n individuals with arbitrary pedigree relation- each QTL position ...founders Bayesian ... See full document
13
Sequential Monte Carlo with transformations
... coalescent model in population genet- ics (Kingman 1982); we consider the case in which we wish to infer the clonal ancestry (or ancestral tree) of a bacterial population from DNA sequence ... See full document
14
Bayesian Inference for PCFGs via Markov Chain Monte Carlo
... The standard methods for inferring the parameters of probabilistic models in computational linguistics are based on the principle of maximum-likelihood esti- mation; for example, the parameters of Probabilistic ... See full document
8
Psychology in econometric models: conceptual and methodological foundations
... Personality, ability, trust, motivation and beliefs determine outcomes in life and in particular those of economic nature such as …nding a job or earnings. A problem with this type of determinants is that they are not ... See full document
32
Sequential Monte Carlo Method Toward Online RUL Assessment with Applications
... (Sequential Monte Carlo) method has been successfully developed and applied in many dif- ferent fields ...optimal Bayesian esti- mation ... See full document
12
Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking
... leader–follower model describes a behavior in which each member of a group interacts with an aggregative, yet virtual ...group model and the group parameter are repre- sented as a deterministic function of ... See full document
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