搜索结果: 1-15 共查到“Bayesian Inference”相关记录31条 . 查询时间(0.109 秒)
Informative Bayesian inference for the skew-normal distribution
Bayesian inference Gibbs sampling Markov Chain Monte Carlo Multivariate skew-normal distribution Stochastic representation of the skew-normal Uni
2013/6/14
Motivated by the analysis of the distribution of university grades, which is usually asymmetric, we discuss two informative priors for the shape parameter of the skew-normal distribution, showing that...
Mean field variational Bayesian inference for support vector machine classification
Approximate Bayesian inference variable selection missing data mixed model Markov chain Monte Carlo
2013/6/14
A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson & Scott (2012) is presented. This representation allows circumvention of ma...
A Markov Model of Machine Translation using Non-parametric Bayesian Inference
Markov Model Machine Translation Non-parametric Bayesian Inference
2014/3/20
Most modern machine translation systems use phrase pairs as translation units, allowing for accurate modelling of phraseinternal translation and reordering. However phrase-based approaches are much le...
A Fast Iterative Bayesian Inference Algorithm for Sparse Channel Estimation
A Fast Iterative Bayesian Inference Algorithm Sparse Channel Estimation
2013/4/27
In this paper, we present a Bayesian channel estimation algorithm for multicarrier receivers based on pilot symbol observations. The inherent sparse nature of wireless multipath channels is exploited ...
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...
Robust Bayesian inference of networks using Dirichlet t-distributions
Bayesian inference Dirichlet process graphical model Markov chain Monte Carlo t-distribution.
2012/9/18
Bayesian graphical modeling provides an appealing way to obtain uncertainty esti-mates when inferring network structures, and much recent progress has been made for Gaussian models. These models have ...
Bayesian Inference and Prediction of Burr Type XII Distribution for Progressive First Failure Censored Sampling
Burr Type XII Distribution Progressive First-Failure Censored Sample Bayesian Estimations Gibbs Sampling Markov Chain Monte Carlo Posterior Predictive Density
2013/1/28
This paper deals with Bayesian inference and prediction problems of the Burr type XII distribution based on progressive first failure censored data. We consider the Bayesian inference under a squared ...
Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference
Machine Learning (stat.ML)
2010/12/17
We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference for continuous-variable graphical models. In contrast to most previous algorithms, our method is prov...
Bayesian inference and model choice in a hidden stochastic two-compartment model of hematopoietic stem cell fate decisions
Stochastic two-compartment model hidden Markov models reversible jump MCMC hematopoiesis stem cell asymmetric division
2010/11/8
Despite rapid advances in experimental cell biology, the in vivo behavior of hematopoietic stem cells (HSC) cannot be directly ob-served and measured. Previously we modeled feline hematopoiesis using ...
We introduce an extension of the P\'olya tree approach for constructing distributions on the space of probability measures. By using optional stopping and optional choice of splitting variables, the ...
Self-Selectivity in Firm’s Decision to Withdraw IPO: Bayesian Inference for Hazard Models of Bankruptcy with Feedback
IPO finance studies survival probabilities
2011/4/1
Examination on firm performance subsequent to a chosen event is widely used in finance studies to analyze the motivation behind managerial decisions. However, results are often subject to bias when th...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a
universal method for summarising uncertainty and making estimates and predictions using
probability statements...
ModernWeb services, such as those at Google, Yahoo!, and Ama-
zon, handle billions of requests per day on clusters of thousands of
computers. Because these services operate under strict performance
...
Bayesian Inference of Stochastic Volatility Model by Hybrid Monte Carlo
Hybrid Monte Carlo Algorithm Stochastic Volatility Model
2010/10/18
The hybrid Monte Carlo (HMC) algorithm is applied for the Bayesian inference of the stochastic volatility (SV) model. We use the HMC algorithm for the Markov chain Monte Carlo updates of volatility va...
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...