搜索结果: 1-14 共查到“统计核算理论 Bayesian”相关记录14条 . 查询时间(0.093 秒)
Bayesian Multi-Dipole Modeling of Single MEG Topographies by Adaptive Sequential Monte Carlo Samplers
Magnetoencephalography inverse problem Multi-object estimation Multi-dipole models Adaptive Sequential Monte Carlo samplers
2013/6/14
We describe a novel Bayesian approach to the estimation of neural currents from a single distribution of magnetic field, measured by magnetoencephalography. We model neural currents as an unknown numb...
Bayesian Functional Generalized Additive Models with Sparsely Observed Covariates
auction data functional data analysis functional regression linear mixed models measurement error MCMC penalized splines variational inference
2013/6/14
The functional generalized additive model (FGAM) was recently proposed in McLean et al. (2012) as a more flexible alternative to the common functional linear model (FLM) for regressing a scalar on fun...
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...
Revisiting Bayesian Blind Deconvolution
Blind deconvolution blind image deblurring variational Bayes sparse priors sparse estimation
2013/6/14
Blind deconvolution involves the estimation of a sharp signal or image given only a blurry observation. Because this problem is fundamentally ill-posed, strong priors on both the sharp image and blur ...
Two General Methods for Population Pharmacokinetic Modeling: Non-Parametric Adaptive Grid and Non-Parametric Bayesian
Population pharmacokinetic modeling non-parametric maximum likelihood Bayesian Stick-breaking Pmetrics RJags
2013/5/2
Population pharmacokinetic (PK) modeling methods can be statistically classified as either parametric or nonparametric (NP). Each classification can be divided into maximum likelihood (ML) or Bayesian...
Conjugate distributions in hierarchical Bayesian ANOVA for computational efficiency and assessments of both practical and statistical significance
ANOVA xed eects random eects variance components hierar-chical Bayes multilevel model constraints
2013/4/27
Assessing variability according to distinct factors in data is a fundamental technique of statistics. The method commonly regarded to as analysis of variance (ANOVA) is, however, typically confined to...
A Greedy Approximation of Bayesian Reinforcement Learning with Probably Optimistic Transition Model
Reinforcement Learning Uncertain Knowledge Probabilistic Reasoning Optimal Behavior in Polynomial Time
2013/5/2
Bayesian Reinforcement Learning (RL) is capable of not only incorporating domain knowledge, but also solving the exploration-exploitation dilemma in a natural way. As Bayesian RL is intractable except...
Infinite-dimensional Bayesian filtering for detection of quasi-periodic phenomena in spatio-temporal data
Infinite-dimensional Bayesian filtering detection of quasi-periodic phenomena spatio-temporal data
2013/4/27
This paper introduces a spatio-temporal resonator model and an inference method for detection and estimation of nearly periodic temporal phenomena in spatio-temporal data. The model is derived as a sp...
Monte-Carlo utility estimates for Bayesian reinforcement learning
Monte-Carlo estimates Bayesian reinforcement learning
2013/5/2
This paper introduces a set of algorithms for Monte-Carlo Bayesian reinforcement learning. Firstly, Monte-Carlo estimation of upper bounds on the Bayes-optimal value function is employed to construct ...
Classification Loss Function for Parameter Ensembles in Bayesian Hierarchical Models
Classification Loss Function Parameter Ensembles Bayesian Hierarchical Models
2011/6/20
Our perspective in this paper follows the framework adopted by Lin et al. (2006), who intro-
duced several loss functions for the identi cation of the elements of a parameter ensemble that
represent...
Density Estimation and Classification via Bayesian Nonparametric Learning of Affine Subspaces
Dimension reduction Classi er Variable selection Nonparametric Bayes
2011/6/20
It is now practically the norm for data to be very high dimensional in areas such as genetics, machine
vision, image analysis and many others. When analyzing such data, parametric models are often to...
Consistent Model Selection of Discrete Bayesian Networks from Incomplete Data
Discrete Bayesian Networks Consistent Model Incomplete Data node-variables
2011/6/20
A maximum likelihood based model selection of discrete Bayesian
networks is considered. The model selection is performed through scoring
function S, which, for a given network G and n-sample Dn, is ...
Asymptotic Behaviour of Approximate Bayesian Estimators
Parameter Estimation Hidden Markov Model Maximum Likelihood Approximate Bayesian Computation Sequential Monte Carlo
2011/6/20
Although approximate Bayesian computation (ABC) has become
a popular technique for performing parameter estimation when the
likelihood functions are analytically intractable there has not as yet
be...
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 ...