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Dirichlet Posterior Sampling with Truncated Multinomial Likelihoods
Dirichlet Posterior Sampling Multinomial Likelihoods
2012/9/18
This document considers the problem of drawing samples from posterior distributions formed under a Dirichlet prior and a truncated multinomial likelihood, by which we mean a Multi-nomial likelihood fu...
Fast MCMC sampling for Markov jump processes and extensions
Markov jump process uniformization MCMC Gibbs sampler Markov-modulated Poisson process continuous-time Bayesian network
2012/9/17
Markov jump processes (or continuous-time Markov chains) are a simple and important class of continuous-time dynamical systems. In this paper, we tackle the problem of simu-lating from the posterior d...
Guaranteed Conservative Fixed Width Confidence Intervals Via Monte Carlo Sampling
Guaranteed Conservative Fixed Width Confidence Intervals Monte Carlo Sampling
2012/9/17
Monte Carlo methods are used to approximate the means,? of random variablesY, whose distributions are not known explicitly. The key idea is that the
average of a random sample,Y1,...,Yn, tends to 礱sn...
How to sample if you must: on optimal functional sampling
Learning Theory Other Applications.
2012/9/17
We examine a fundamental problem that models various active sampling setups, such as network tomography. We analyze sampling of a multivariate normal distribution with an unknown expectation that need...
Dealing with nonresponse in survey sampling: a latent modeling approach
unit nonresponse item nonresponse latent trait models response propensity non-ignorable nonresponse
2012/9/19
Nonresponse is present in almost all surveys and can severely bias estimates. It is usually distinguished between unit and item nonresponse: in the former, we completely fail to have information from ...
An estimation of distribution algorithm with adaptive Gibbs sampling for unconstrained global optimization
Estimation of distribution algorithms Evolutionary algorithms
2011/7/19
In this paper is proposed a new heuristic approach belonging to the field of evolutionary Estimation of Distribution Algorithms (EDAs). EDAs builds a probability model and a set of solutions is sample...
Learning with the Weighted Trace-norm under Arbitrary Sampling Distributions
Learning Weighted Trace-norm Arbitrary Sampling Distributions
2011/7/7
We provide rigorous guarantees on learning with the weighted trace-norm under arbitrary sampling distributions.
Distribution fitting 12. Sampling distribution of compounds abundance from plant species measured by instrumentation. Application to plants metabolism classification
chemical compounds abundances lognormal distribution
2011/7/6
A series of ten plant species belonging to Magnoliopsida - Dicotyledons class were analyzed in terms of chemical compounds distribution of abundance, starting from the assumption that these distributi...
Essentially ML ASN-Minimax double sampling plans
Acceptance sampling by variables ASN-Minimax double sampling plan
2011/7/6
Subject of this paper is ASN-Minimax (AM) double sampling plans by variables for a normally distributed quality characteristic with unknown standard deviation and two-sided specification limits.
Improved estimator of the entropy and goodness of fit tests in ranked set sampling
Ordered Ranked set sampling Judgement ranking Order statistic Information theory
2011/7/5
The entropy is one of the most applicable uncertainty measures in many statistical and en- gineering problems. In statistical literature, the entropy is used in calculation of the Kullback- Leibler (K...
Reconstruction of Fractional Brownian Motion Signals From Its Sparse Samples Based on Compressive Sampling
Compressive Sampling fractional Brownian motion interpolation financial time-series fractal
2011/6/21
This paper proposes a new fBm (fractional Brownian
motion) interpolation/reconstruction method from partially
known samples based on CS (Compressive Sampling). Since 1/f
property implies power law ...
Efficient sampling of high-dimensional Gaussian fields: the non-stationary / non-sparse case
Efficient sampling high-dimensional Gaussian non-stationary non-sparse case
2011/6/20
This paper is devoted to the problem of sampling Gaussian fields
in high dimension. Solutions exist for two specific structures of inverse
covariance : sparse and circulant. The proposed approach is...
State-Observation Sampling and the Econometrics of Learning Models
Hidden Markov model particle filter state-observation sampling learning indirect inference forecasting state space model value at risk
2011/6/20
In nonlinear state-space models, sequential learning about the hidden state can proceed
by particle filtering when the density of the observation conditional on the state is available
analytically (...
Optimum allocation in multivariate stratified random sampling: Stochastic matrix optimisation
Multivariate stratified random sampling modified E-model stochastic programming optimum allocation integer programming E-model V -model P-model
2011/6/17
The allocation problem for multivariate stratified random sampling as a problem of
stochastic matrix integer mathematical programming is considered. With these aims
the asymptotic normality of sampl...
Optimal Multistage Sampling in a Boundary-Crossing Problem
Asymptotic Brownian motion Group sequential Multistage Optimality
2011/6/17
Brownian motion with known positive drift is sampled in stages until
it crosses a positive boundary a. A family of multistage samplers that con-
trol the expected overshoot over the boundary by vary...