stochastic search variable selection
Stochastic Search Variable Selection for Identifying Multiple Quantitative Trait Loci
... utilize stochastic search variable selection methodology to develop a Bayesian method for identifying multiple quantitative trait loci (QTL) for complex traits in experimental ...
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Bayesian Forecasting using Stochastic Search Variable Selection in a VAR Subject to Breaks
... Despite the success of VARs relative to many alternatives, two problems continue to undermine their forecast performance. These are the problems of structural breaks and over-parameterization. The purpose of this paper ...
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Stochastic Search Variable Selection in Vector Error Correction Models with an Application to a Model of the UK Macroeconomy
... scribed as working with a single very flexible model. The problems with such models are that they risk over-fitting and typically involve a large number of parameters which can be difficult to estimate with any ...
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Stochastic Search Variable Selection in Vector Error Correction Models with an Application to a Model of the UK Macroeconomy
... scribed as working with a single very flexible model. The problems with such models are that they risk over-fitting and typically involve a large number of parameters which can be difficult to estimate with any ...
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Stochastic Search Variable Selection in Vector Error Correction Models with an Application to a Model of the UK Macroeconomy
... scribed as working with a single very flexible model. The problems with such models are that they risk over-fitting and typically involve a large number of parameters which can be difficult to estimate with any ...
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Comparison between the stochastic search variable selection and the least absolute shrinkage and selection operator for genome wide association studies of rheumatoid arthritis
... model selection algorithms are urgently needed. Two popular model selection methods have been proposed: the stochastic search variable selection (SSVS) [3] and the least absolute ...
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Bayesian forecasting using stochastic search variable selection in a VAR subject to breaks
... ωηερε ισ υππερ−τριανγυλαρ. Τηε ΣΣςΣ πριορ ινϖολϖεσ υσινγ α στανδαρδ Γαmmα πριορ φορ σθυαρε οφ εαχη οφ τηε διαγοναλ ελεmεντσ οφ ανδ τηε ΣΣςΣ mιξτυρε οφ νορmαλσ πριορ φορ εαχη ελεmεντ αβοϖ[r] ...
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Bayesian Variable Selection via Particle Stochastic Search
... a stochastic search variable selection (SSVS) algo- rithm for normal linear regression using Gibbs sampling to search Γ for high posterior probability ...
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Selection of Multivariate Stochastic Volatility Models via Bayesian Stochastic Search
... comparison. Variable selection problem in a univariate model with m explanatory variables involves comparing 2 m competing ...MCMC stochastic search algorithm in univariate regression ...
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A variable neighborhood search simheuristic for project portfolio selection under uncertainty
... and stochastic PPSPs, and compare the near- optimal ...the stochastic PPSP, we find that a portfolio of projects in a stochastic environment always yields a lower (expected) NPV than a portfolio in a ...
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A variable neighborhood search simheuristic for project portfolio selection under uncertainty
... and stochastic PPSPs, and compare the near- optimal ...the stochastic PPSP, we find that a portfolio of projects in a stochastic environment always yields a lower (expected) NPV than a portfolio in a ...
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Stochastic search
... Stochastic global optimizers, such as simulated annealing, are simple to use, provably robust, more efficient than exhaustive search, and more general than special purpose heuristics.. R[r] ...
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Stochastic Search Equilibrium
... to break ranks in the distribution of competing offers, which then coincides with the given distribution of firm productivities or initial sizes. This immediately implies our main result that equilibrium, if it exists, ...
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Robust Variable Selection
... from a heavy tailed distribution. In Table 2.2, we see that both of the model error ratios are similar across the five robust VAMS procedures. This is a result we expect, since many of these procedures are only slight ...
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Iterated Local Search. Variable Neighborhood Search
... reactive search algorithm which fits into ILS framework; tabu search was used in perturbation phase; good performance also due to good tie-breaking criterion weighted MAX-SAT Smith, Hoos, Stützle, 2002 ...
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Stochastic Diffusion Search Review
... NEural STochastic diffusion search net- wORk – consists of an artificial retina, a layer of fully connected matching neurons and retinotopically organised memory ...the search space results in the ...
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Stochastic Local Search Algorithms
... Knowledge Representation Machine Learning Reasoning + Decision Theory + Robotics + Human Computer /Robot Interaction Natural Language Generation. Mix of knowledge represent[r] ...
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Information theoretic stochastic search
... episode. Stochastic search algorithms [ 32 , 92 , 83 ] typically maintain a search distribution over the parameters that we want to ...This search distribution is used to create samples of the ...
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A proxy-variable search procedure
... proxy-variable search procedure, based on a sensitivity analysis framework, aiming to provide a useful tool for the applied researcher whenever he faces measurement or proxy-variable ...
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Local termination criteria for Stochastic Diffusion Search : a comparison with the behaviour of ant nest-site selection
... elva.robinson@york.ac.uk https://www.york.ac.uk/biology/research/ecology-evolution/elva-robinson/ Abstract. Population based decision mechanisms employed by many Swarm Intelligence methods can suffer poor convergence ...
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