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Comparative investigation of three Bayesian p values
Bayesian model checking Posterior predictive p value Sampled posterior p value
2016/1/26
Please check your proof carefully and mark all corrections at the appropriate place in the proof (e.g., by using on-screen annotation in the PDF file) or compile them in a separate list. Note: if you ...
Comparative investigation of three Bayesian p values
Bayesian model checking Posterior predictive p value Sampled posterior p value
2016/1/20
Bayesian p values are a popular and important class of approaches for Bayesian model checking. They are used to quantify the degree of surprise from the observed data given the specified data model an...
In this letter we revisit the problem of optimal design of quantum tomographic experiments. In contrast to previous approaches where an optimal set of measurements is decided in advance and then kept ...
A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data
Bayesian Approach MISR Data Retrieval
2011/7/19
Atmospheric aerosols can cause serious damage to human health and life expectancy. Using the radiances observed by NASA's Multi-angle Imaging SpectroRadiometer (MISR), the current MISR operational alg...
abc: an R package for Approximate Bayesian Computation (ABC)
abc package Approximate Bayesian Computation
2011/7/7
Many recent statistical applications involve inference under complex models, where it is computationally prohibitive to calculate likelihoods but possible to simulate data.
Bayesian and L1 Approaches to Sparse Unsupervised Learning
Bayesian L1 Approaches Sparse Unsupervised Learning
2011/7/6
The use of L1 regularisation for sparse learning has generated immense research interest, with successful application in such diverse areas as signal acquisition, image coding, genomics and collaborat...
Bayesian multitask inverse reinforcement learning
Bayesian inference multitask learning inverse reinforce-ment learning
2011/7/6
We generalise the problem of inverse reinforcement learning to multiple tasks, from a set of demonstrations. Each demonstration may represent one expert trying to solve a different task.
Discussion of "Statistical Inference: The Big Picture" by R. E. Kass [arXiv:1106.2895]
Sparse Bayesian Methods for Low-Rank Matrix Estimation
Low-Rank Matrix Estimation Sparse Bayesian Methods
2011/3/24
Recovery of low-rank matrices has recently seen significant activity in many areas of science and engineering, motivated by recent theoretical results for exact reconstruction guarantees and interesti...
Bayesian nonparametric estimation and consistency of mixed multinomial logit choice models
Bayesian consistency blocked Gibbs sampler discrete choice models mixed multinomial logit random probability measures stick-breaking priors
2011/3/24
This paper develops nonparametric estimation for discrete choice models based on the mixed multinomial logit (MMNL) model. It has been shown that MMNL models encompass all discrete choice models deriv...
Consistency of Bayesian Linear Model Selection With a Growing Number of Parameters
Bayesian model selection growing number of parameters Posterior model consistency consistency of Bayes factor consistency of posterior odds ratio Gibbs sampling
2011/3/18
Linear models with a growing number of parameters have been widely used in modern statistics. One important problem about this kind of model is the variable selection issue. Bayesian approaches, which...
A Bayesian approach to comparing theoretic models to observational data: A case study from solar flare physics
A Bayesian approach observational data solar flare physics
2011/3/25
Solar flares are large-scale releases of energy in the solar atmosphere, which are characterized by rapid changes in the hydrodynamic properties of plasma from the photosphere to the corona. Solar phy...