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In this note the relation between the range-renewal speed and entropy for i.i.d. models is discussed.
Information Criteria for Deciding between Normal Regression Models
Information Criteria Deciding between Normal Regression Models
2013/6/14
Regression models fitted to data can be assessed on their goodness of fit, though models with many parameters should be disfavored to prevent over-fitting. Statisticians' tools for this are little kno...
A General Bernstein--von Mises Theorem in semiparametric models
A General Bernstein von Mises Theorem semiparametric models
2013/6/14
A Bernstein-von Mises theorem is derived for general semiparametric functionals. The result is applied to a variety of semiparametric problems, in i.i.d. and non-i.i.d. situations. In particular, new ...
Efficient Algorithms for Multivariate Linear Mixed Models in Genome-wide Association Studies
Efficient Algorithms Multivariate Linear Mixed Models Genome-wide Association Studies
2013/6/17
Multivariate linear mixed models (mvLMMs) have been widely used in many areas of genetics, and have attracted considerable recent interest in genome-wide association studies (GWASs). However, existing...
Quantum Annealing for Dirichlet Process Mixture Models with Applications to Network Clustering
Quantum annealing Dirichlet process Stochastic optimization Maximum a posteriori estimation Bayesian nonparametrics
2013/6/17
We developed a new quantum annealing (QA) algorithm for Dirichlet process mixture (DPM) models based on the Chinese restaurant process (CRP). QA is a parallelized extension of simulated annealing (SA)...
Dynamic Covariance Models for Multivariate Financial Time Series
Dynamic Covariance Models Multivariate Financial Time Series
2013/6/14
The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data. However, some of the most popular models suffer from a) overfitting problem...
Factored expectation propagation for input-output FHMM models in systems biology
input output factored hidden Markov models approximate inference variational inference expectation propagation systems biology microarray data
2013/6/14
We consider the problem of joint modelling of metabolic signals and gene expression in systems biology applications. We propose an approach based on input-output factorial hidden Markov models and pro...
Sparse approximations in spatio-temporal point-process models
latent Gaussian models linear dynamical systems log Gaussian Cox process approximate inference expectation propagation sparse inference
2013/6/14
Analysis of spatio-temporal point patterns plays an important role in several disciplines, yet inference in these systems remains computationally challenging due to the high resolution modelling gener...
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...
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data
Hierarchically-coupled hidden Markov models learning kinetic rates single-molecule data
2013/6/14
We address the problem of analyzing sets of noisy time-varying signals that all report on the same process but confound straightforward analyses due to complex inter-signal heterogeneities and measure...
Calibration diagnostics for point process models via the probability integral transform
Calibration diagnostics point process models probability integral transform
2013/6/14
We propose the use of the probability integral transform (PIT) for model validation in point process models. The simple PIT diagnostics assess the calibration of the model and can detect inconsistenci...
HRF estimation improves sensitivity of fMRI encoding and decoding models
fMRI hemodynamic HRF GLM BOLD en-coding decoding
2013/6/14
Extracting activation patterns from functional Magnetic Resonance Images (fMRI) datasets remains challenging in rapid-event designs due to the inherent delay of blood oxygen level-dependent (BOLD) sig...
MCMC methods for Gaussian process models using fast approximations for the likelihood
MCMC methods for Gaussian process models using fast approximations for the likelihood
2013/6/14
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
MCMC methods for Gaussian process models using fast approximations for the likelihood
MCMC methods for Gaussian process models using fast approximations for the likelihood
2013/6/14
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
Switching Nonparametric Regression Models and the Motorcycle Data revisited
nonparametric regression machine learning mixture of Gaussian processes latent variables EM algorithm motorcy-cle data
2013/6/14
We propose a methodology to analyze data arising from a curve that, over its domain, switches among J states. We consider a sequence of response variables, where each response y depends on a covariate...
