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Statistical modelling of summary values leads to accurate Approximate Bayesian Computations
Statistical modelling summary values leads accurate Approximate Bayesian Computations
2013/6/14
Approximate Bayesian Computations (ABC) are considered to be noisy. We show that ABC can be set up to estimate the mode of the true posterior density exactly, or alternatively provide unbiased estimat...
Convergence and asymptotic normality of variational Bayesian approximations for exponential family models with missing values
Convergence asymptotic normality variational Bayesian approximations exponential family models missing values
2012/9/19
We study the properties of variational Bayes approximations for exponential family mod-els with missing values. It is shown that the iterative algorithm for obtaining the varia-tional Bayesian estimat...
Early, reliable detection of disease outbreaks is a critical problem today. This paper reports an investigation of the use of causal Bayesian
networks to model spatio-temporal patte...
A Widely Applicable Bayesian Information Criterion
Bayes marginal likelihood Widely applicable Bayes Information Criterion
2012/9/18
A statistical model or a learning machine is called regular if the map taking a pa-rameter to a probability distribution is one-to-one and if its Fisher information matrix is always positive definite....
Bayesian astrostatistics: a backward look to the future
Bayesian astrostatistics backward look the future
2012/9/18
This volume contains presentations from the first invited session on astrostatistics to be held at an International Statistical Institute (ISI) World Statistics Congress. This session was a major mile...
Comment on "Bayesian astrostatistics: a backward look to the future" by Tom Loredo
Comment "Bayesian astrostatistics: a backward look to the future" Tom Loredo
2012/9/18
This short note points out two of the incongruences that I findin the Loredo (2012) comments on Andreon (2012), i.e. on my chapter written for the book “Astrostatistical Challenges for the New Astrono...
Bayesian Analysis of Multiway Tables in Association Studies: A Model Comparison Approach
Bayesian Analysis Multiway Tables Association Studies Model Comparison Approach
2012/9/17
We consider the problem of statistical inference on unknown quantities structured as a multiway table. We show that such multiway tables are naturally formed by arranging regression coecients in comp...
The Dependence of Routine Bayesian Model Selection Methods on Irrelevant Alternatives
Bayesian Model Selection Methods Alternatives
2012/9/17
Bayesian methods - either based on Bayes Factors or BIC - are now widely used for model selection. One property that might reasonably be demanded of any model
selection method is that if a modelM1 is...
Bayesian inference on dependence in multivariate longitudinal data
Cholesky decomposition covariance matrix moment-matching oxidative stress random effects shrinkage prior.
2012/9/17
In many applications, it is of interest to assess the dependence structure in multivariate longitudinal data. Discovering such dependence is challenging
due to the dimensionality involved. By concate...
A Bayesian Analysis of the Correlations Among Sunspot Cycles
Bayesian Analysis Correlations Among Sunspot Cycles
2012/9/18
Sunspot numbers form a comprehensive, long-duration proxy of so-lar activity and have been used numerous times to empirically investigate the properties of the solar cycle. A number of correlations ha...
Bayesian semi-parametric forecasting with penalised splines and autoregressive errors
splines, autoregressive errors, semi-parametric regression, Bayesian
2012/9/18
Observational time series data often exhibit both cyclic temporal trends and autocorrelation and may also depend on covariates. As such, there is a need for exible regression models that are able to c...
PAC-Bayesian Estimation and Prediction in Sparse Additive Models
Additive models sparsity regression estimation PAC-Bayesian bounds oracle inequality MCMC stochastic search.
2012/9/17
The present paper is about estimation and prediction in high-dimensional additive models under a sparsity assumption (p nparadigm).A PAC-Bayesian strategy is investigated, delivering oracle inequaliti...
Bayesian Mode Regression
Bayesian inference empirical likelihood Markov Chain Monte Carlo methods mode
2012/9/17
Like mean, quantile and variance, mode is also an important measure of central tendency and data summary. Many practical questions often focus on “Which element (gene or file or signal) occurs most of...
A note on Bayesian credible sets in restricted parameter space problems and lower bounds for frequentist coverage
Bayesian methods Credible sets Frequentist coverage probability Lower bound Restricted Parameter Spending function
2012/9/17
For estimating a lower bounded parametric function in the framework of Marchand and Strawderman(2006), we provide “through” a unified approach a class of Bayesian confidence intervals with credibility...
Structure-Based Bayesian Sparse Reconstruction
Structure-Based Bayesian Sparse Reconstruction
2012/9/19
Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the spars...