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Bayesian Data Analysis
TLDR
Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Probabilistic Topic Models
In this article, we review probabilistic topic models: graphical models that can be used to summarize a large collection of documents with a smaller number of distributions over words. Those
High cumulative incidence of uterine leiomyoma in black and white women: ultrasound evidence.
TLDR
It is suggested that most black and white women in the United States develop uterine fibroid tumors before menopause and that uterine Fibroid tumors develop in black women at earlier ages than in white women.
GENERALIZED DOUBLE PARETO SHRINKAGE.
TLDR
The properties of the maximum a posteriori estimator are investigated, as sparse estimation plays an important role in many problems, connections with some well-established regularization procedures are revealed, and some asymptotic results are shown.
Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images
TLDR
Nonparametric Bayesian methods are considered for recovery of imagery based upon compressive, incomplete, and/or noisy measurements and significant improvements in image recovery are manifested using learned dictionaries, relative to using standard orthonormal image expansions.
Sparse Bayesian infinite factor models.
TLDR
This work proposes a multiplicative gamma process shrinkage prior on the factor loadings which allows introduction of infinitely many factors, with the loadings increasingly shrunk towards zero as the column index increases, and develops an efficient Gibbs sampler that scales well as data dimensionality increases.
Dirichlet–Laplace Priors for Optimal Shrinkage
TLDR
This article proposes a new class of Dirichlet–Laplace priors, which possess optimal posterior concentration and lead to efficient posterior computation.
Multitask Compressive Sensing
TLDR
It has been demonstrated that with appropriate design of the compressive measurements used to define v, the decompressive mapping vrarru may be performed with error with asymptotic properties analogous to those of the best adaptive transform-coding algorithm applied in the basis Psi.
Nonparametric Bayes Modeling of Multivariate Categorical Data
  • D. Dunson, C. Xing
  • Mathematics, Medicine
    Journal of the American Statistical Association
  • 1 September 2009
TLDR
This article develops a nonparametric Bayes approach, which defines a prior with full support on the space of distributions for multiple unordered categorical variables, and shows this can be accomplished through a Dirichlet process mixture of product multinomial distributions, which is also a convenient form for posterior computation.
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