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We express the classic ARMA time-series model as a directed graphical model. In doing so, we find that the deterministic re-lationships in the model make it effectively impossible to use the EM algori...
We study the properties of variational Bayes approximations for exponential family mod-els with missing values. It is shown that the iterative algorithm for obtaining the varia-tional Bayesian estimat...
In this paper we describe a general probabilis-tic framework for modeling waveforms such as heartbeats from ECGdata. The model is based on segmental hidden Markov mod-els(as usedin speechrecognition) ...
In this paper we describe a general probabilis-tic framework for modeling waveforms such as heartbeats from ECGdata. The model is based on segmental hidden Markov mod-els(as usedin speechrecognition) ...
Consider a nonlinear regression model :yi =g(xi, 兤) +ei, i = 1, ..., n,where thexi are random predictorsxi and兤is the unknown parameter vector ranging in a set set 儲伡Rp. All known results on the consi...
We consider a general nonparametric regression model called the compound model. It includes,as special cases, sparse additive regression and nonparametric (or linear) regression with many covariates b...
TheMacaulay2packageGraphicalModelscontains algorithms for the algebraic study of graphical models associated to undirected, directed and mixed graphs, and associated collections of conditional indepen...
Blood and tissue are composed of many functionally distinct cell subsets. In immunological studies, these can only be measured accurately using single-cell assays. The characterization of these small ...
This paper addresses the problem of Monte Carlo approximation of posterior probability distributions. In particular, we have considered a recently proposed technique known as population Monte Carlo (P...
We consider penalized estimation in hidden Markov models (HMMs) with multi-variate Normal observations. In the moderate-to-large dimensional setting, estimation for HMMs remains challenging in practic...
Variational Bayes computational methods are attracting increasing in-terest because of their ability to scale to large data sets. Here, application of the non-conjugate variational message passing (N...
We develop a framework for modelling and reasoning with uncertainty based on accept and reject statements about gambles. It generalises the frameworks found in the literature based on statements of ac...
In this article, we discuss the composite likelihood estimation of sparse Gaussian graph-ical models. When there are symmetry constraints on the concentration matrix or partial correlation matrix, the...
Many common diseases are highly polygenic, modulated by a large number genetic factors with small effects on suscep-tibility to disease. These small effects are difficult to map reliably in genetic as...
This paper discusses asymptotic theory for penalized spline estimators in generalized additive models. The purpose of this paper is to establish the asymptotic bias and variance as well as the asympto...

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