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Bayesian inference for the MAPK/ERK pathway by considering the dependency of the kinetic parameters
MCMC MAPK/ERKpathway diusion approximation data augmentation dependency in diusion matrix
2009/9/22
The MAPK/ERK pathway is one of the major signal transduction systems which regulates the cellular growth control of all eukaryotes like the cell proliferation and the apoptosis. Because of its importa...
Bayesian Inference for Shape Mixtures of Skewed Distributions, with Application to Regression Analysis
Posterior analysis regression model shape parameter skewness symmetry
2009/9/22
We introduce a class of shape mixtures of skewed distributions and
study some of its main properties. We discuss a Bayesian interpretation and some
invariance results of the proposed class. We devel...
Semi-parametric Bayesian Inference for Multi-Season Baseball Data
Dirichlet Process Partial Exchangeability Semiparametric Random Effects
2009/9/22
We analyze complete sequences of successes (hits, walks, and sacrices)
for a group of players from the American and National Leagues, collected over
4 seasons. The goal is to describe how players pe...
Hidden Markov Dirichlet Process: Modeling Genetic Inference in Open Ancestral Space
Dirichlet Process Hierarchical DP hidden Markov model MCMC statistical genetics recombination population structure SNP
2009/9/22
We present a new statis-
tical framework called hidden Markov Dirichlet process (HMDP) to jointly model
the genetic recombinations among a possibly innite number of founders and the
coalescence-wit...
Bayesian inference for an extended simple regression measurement error model using skewed priors
Berkson model non-informative prior non-random sample pseudo-Bayes factor regression calibration structural error model Winbugs
2009/9/22
In this paper, we introduce a Bayesian extended regression model
with two-stage priors when the covariate is positive and measured with error.
Connections are made with some results in Arellano-Vall...
Statistical inference from set-valued observations
Statistical inference set-valued observations
2009/9/22
Consider a random experiment whose true (unknown)
outcome is modelled by a certain random element X and the available
imprecise observations are modelled by some random set A such that
XE A almost ...
Variational inference for Dirichlet process mixtures
Dirichlet processes hierarchical models variational inference image processing Bayesian computation
2009/9/21
Dirichlet process (DP) mixture models are the cornerstone of non-
parametric Bayesian statistics, and the development of Monte-Carlo Markov chain
(MCMC) sampling methods for DP mixtures has enabled ...
When did Bayesian Inference become "Bayesian"?
Bayes' Theorem Classical statistical methods Frequentist methods Neo-Bayesian revival Stigler's Law of Eponymy
2009/9/21
While Bayes theorem has a 250-year history, and the method of in-
verse probability that owed from it dominated statistical thinking into the twen-
tieth century, the adjective Byesian was not part ...
EMPIRICAL LIKELIHOOD INFERENCE FOR SURVIVAL RATE REGRESSION WITH MISSING INFORMATION PRINCIPLE
Confidence region conditional Kaplan–Meier estimator link function right censoring
2009/9/18
Recently, regression model for the long-term survival probabilities
of patients was proposed, and a semiparametric inference procedure
was developed based on missing information principle. In this p...
Bayesian Inference of a Linear Segmented Regression Model
Bayesian Inference a Linear Segmented Regression Model
2009/9/17
Bayesian Inference of a Linear Segmented Regression Model。
A Bayesian Inference for the Extended Skew-Normal Measurement Error Model
Bayesian Inference Skew-Normal Measurement Error Model
2009/9/17
A Bayesian Inference for the Extended Skew-Normal Measurement Error Model。
Bayesian Inference for the Skewness Parameter of the Scalar Skew-Normal Distribution
Bayesian Inference the Skewness Parameter Skew-Normal Distribution
2009/9/17
Bayesian Inference for the Skewness Parameter of the Scalar Skew-Normal Distribution。
A practical illustration of the importance of realistic individualized treatment rules in causal inference
Experimental Treatment Assignment assumption positivity assumption dynamic treatment rules physical activity
2009/9/16
The effect of vigorous physical activity on mortality in the elderly is difficult to estimate using conventional approaches to causal inference that define this effect by comparing the mortality risks...
Bayesian inference with rescaled Gaussian process priors
Rate of convergence Bayesian inference nonparametric density estimation nonparametric regression classification
2009/9/16
We use rescaled Gaussian processes as prior models for functional parameters in nonparametric statistical models. We show how the rate of contraction of the posterior distributions depends on the scal...