搜索结果: 1-15 共查到“理论统计学 priors”相关记录18条 . 查询时间(0.078 秒)
Adaptive Priors based on Splines with Random Knots
Adaptive estimation bayesian non-parametric optimal contrac-tion rate spline random knots
2013/4/27
Splines are useful building blocks when constructing priors on nonparametric models indexed by functions. Recently it has been established in the literature that hierarchical priors based on splines w...
Besov priors for Bayesian inverse problems
Bayesian inverse problems Fernique-like theorem
2011/6/16
We consider the inverse problem of estimating a function u from
noisy, possibly nonlinear, observations. We adopt a Bayesian approach to the
problem and widen the existing theory, which is developed...
Generalized Species Sampling Priors with Latent Beta reinforcements
Statistics Theory (math.ST) Learning (cs.LG) Methodology (stat.ME)
2010/12/17
Many popular Bayesian Nonparametric priors can be characterized in terms of exchangeable species sampling sequences. One example is the Dirichlet Process prior, that has been increasingly used for mod...
Asymptotic admissibility of priors and elliptic differential equations
Asymptotic admissibility priors elliptic differential equations
2010/3/11
We evaluate priors by the second order asymptotic behaviour of the
corresponding estimators. Under certain regularity conditions, the risk di erences
between ecient estimators of parameters taking ...
Mirror averaging with sparsity priors
Mirror averaging progressive mixture sparsity aggregation of estimators oracleinequalities
2010/3/11
We consider the problem of aggregating the elements of a (possibly infinite) dictionary
for building a decision procedure, that aims at minimizing a given criterion. Along with the
dictionary, an in...
Bayesian inferences in high energy physics often use uniform prior distributions for parameters
about which little or no information is available before data are collected. The resulting posterior
d...
On some Bayesian nonparametric estimators for species richness under two-parameter Poisson-Dirichlet priors
Bayesian nonparametric estimators species richness two-parameter Poisson-Dirichlet priors
2010/3/11
We present an alternative approach to the Bayesian nonparametric analysis of conditional
species richness under two-parameter Poisson Dirichlet priors. We rely on a known characteri-
zation by delet...
In the literature surrounding Bayesian penalized regression, the two primary choices of
prior distribution on the regression coecients are zero-mean Gaussian and Laplace. While
both have been compa...
Relaxation Penalties and Priors for Plausible Modeling of Nonidentified Bias Sources
Bias biostatistics causality epidemiology measurement error misclassification observational studies odds ratio relative risk
2010/3/9
In designed experiments and surveys, known laws or de-
sign feat ures provide checks on the most relevant aspects of a model
and identify the target parameters. In contrast, in most observational
s...
Bayesian Variable Selection and Computation for Generalized Linear Models with Conjugate Priors
Bayes factor Conditional Predictive Ordinate Conjugate prior Poisson regression Logistic regression
2009/9/22
In this paper, we consider theoretical and computational connections
between six popular methods for variable subset selection in generalized linear
models (GLMs) Under the conjugate priors develope...
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...
Integral priors for the one way random effects model
Bayesian model selection Integral priors Intrinsic priors Random effects model Recurrent Markov chains
2009/9/22
The one way random effects model is analyzed from the Bayesian
model selection perspective. From this point of view Bayes factors are the key
tool to choose between two models. In order to produce ...
Natural and modified conjugate priors in exponential families of stochastic processes
Natural and modified conjugate exponential families stochastic processes
2009/9/21
Modified eonjugate families OF prior distributiorrs are
ia-uestigatcd and their propefiies are examined in the context d applica~
ons trr admissible md mi-dx eestEmatiaa far the general cxponentid
...
Proper Priors Yielding Linear Bayes Estimators for the Natural Parameter of an Exponential Family
Linear Bayes Estimators Natural Parameter an Exponential Family
2009/9/18
Proper Priors Yielding Linear Bayes Estimators for the Natural Parameter of an Exponential Family。
Expected Posterior Priors in Factor Analysis。