[PDF] Top 20 Computing strategies for complex Bayesian models
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Computing strategies for complex Bayesian models
... decades, Bayesian statistics has seen a rise in popularity due to the large availability of powerful personal ...a Bayesian viewpoint often underlaid the use of simplified models so that an ... Voir le document complet
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A Distributed Computing Workflow for Modelling Environmental Flows in Complex Terrain
... terrain models are re- quired. However, terrain models can be difficult and costly to acquire, and often lack detail of important flow steering structures such as bridges or ...workflow for re- ... Voir le document complet
13
Mean-field variational approximate Bayesian inference for latent variable models
... However, for large and complex models, simulation methods are often quite expensive, in terms of time and storage; in particular MCMC algorithms still exhibit technical difficulties, especially with ... Voir le document complet
14
Non-incremental strategies for simulating thermomechanical models with uncertainty
... applied for treating some multi-dimensional models encountered in the kinetic theory description of complex fluids [1] [2] [3] and for treating high resolution thermal homogenization ...[5]. ... Voir le document complet
5
Model Selection for Mixture Models-Perspectives and Strategies
... By way of illustration, a mixture of univariate Gaussian distributions is used for Bayesian clustering of the galaxy data (Roeder, 1990), assuming that G = 5 is fixed. Prior specifi- cation follows ... Voir le document complet
40
Bayesian codon models for detecting convergent molecular adaptation
... the complex molecular evolutionary patterns displayed by the Rubisco gene in eudicots represent an exciting case-study for assessing and comparing current codon modeling strategies (Kapralov, Smith ... Voir le document complet
179
Bayesian nonparametric latent variable models
... the Bayesian paradigm exploits the laws of probability to represent current states of ...mathematics for updating the knowledge based on the data are well- ...several models and consequently ... Voir le document complet
166
Bayesian Mixture Models For Semi-Supervised Clustering
... with Bayesian finite and infinite mixture mod- ...mixture models like the categorical mixture model, latent Dirichlet allocation or topic ...more complex probabilistic graphical ... Voir le document complet
8
Acceleration Strategies of Markov Chain Monte Carlo for Bayesian Computation
... 5.4 Conclusion Our approach combines divide-and-conquer MCMC methods, random forest learn- ing, and importance sampling simulation, to scale MCMC algorithms. Unlike ex- isting divide-and-conquer MCMC methods, we propose ... Voir le document complet
143
Analysis of bayesian and frequentist strategies for sequential resource allocation
... bandit models was known before any theoretical guarantee even in the Bernoulli case was ...bounds for Thompson Sampling in this ...policy for this sampled MDP is computed and played until the end of ... Voir le document complet
213
Computing and fabricating multilayer models
... Figure 16: A MRI dataset fabricated using our volumetric resampling algorithm. Left: Traditional 170 slice rendering. Right: three views of a printout formed from a stack of 17 acrylic tiles. We plan to adapt these core ... Voir le document complet
9
Bayesian Inference for Periodic Regime-Switching Models
... of computing and in many cases become in- ...a Bayesian approach via the Gibbs ...(1992) for an introduction to Gibbs sampling and McCulloch and Tsay (1994) for its use in Markov switching ... Voir le document complet
21
A Bayesian Approach for 3D Models Retrieval Based on Characteristic Views
... Mahmoudi and Daoudi [4] also suggest to use the CSS from the outlines of the 3D model extracted vie6s. The CSS is then organized in a tree structure called M . Chen & Stockman [5] as 6ell as Yi and al. [6] propose a ... Voir le document complet
5
Bayesian classifcation of events for task labeling using workfow models
... algorithms for discover- ing causal relations in activates and complex constructs [4, 3], efficient methods for analyzing large logs [3], and user friendly visualization of discovered work- flow ... Voir le document complet
14
Bayesian models for screening and diagnosis of pulmonary disease
... In contrast, the estimates from the expert-derived network are easy to procure if one has access to domain experts. Because of time constraints, we were only able to survey five pulmonologists. However, it would not be ... Voir le document complet
107
Bayesian Inference for Generalised Markov Switching Stochastic Volatility Models
... MSSV models are more difficult to estimate than simple continuous SV models because there are two hidden levels in the latent ...SV models score function cannot be evaluated and the choice of the ... Voir le document complet
47
Late Fusion of Bayesian and Convolutional Models for Action Recognition
... to its 2D CNN counterpart. C3D networks extract a global 150 descriptor from the clip independently of the action that took 151 place previously. This is particularly suitable and shows strong 152 results for ... Voir le document complet
9
Acquisition strategies for commonality across complex aerospace systems-of-systems
... For systems which NASA can design now and which will not change across the different projects in the exploration architecture NASA should begin a "Build-to- Print&#[r] ... Voir le document complet
208
A stochastic game framework for analyzing computational investment strategies in distributed computing
... time for which the center decides to run the system is exponentially distributed with rate parameter β, where β is a ...constant. For theoretical interest, one could consider a generalization where the ... Voir le document complet
10
Strategies for Getting the Highest Likelihood in Mixture Models
... Unité de recherche INRIA Rhône-Alpes 655, avenue de l’Europe - 38330 Montbonnot-St-Martin France Unité de recherche INRIA Lorraine : LORIA, Technopôle de Nancy-Brabois - Campus scientifi[r] ... Voir le document complet
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