Most existing content-based filtering approaches learn user profiles independently without capturing the similarity among users. Bayesian hierarchical models [42] learn user profiles jointly and have the advantage of being able to borrow discriminative information from other users through a Bayesian prior. However, the standard Bayesian hierarchical models assume all user profiles are generated...

1 Mixture Model and Bayesian Model Interpretations Combining Equations 2.1 and 2.2 (Main Text), we can consider the empirical-Bayes hierarchical model as a mixed model with both fixed and random effects in the frequentist setting as given by: logit(pmi) = αm + ZmγXmi +Xmiτ tmm, where ZmγXmi would be considered a fixed effect and Xmiτ tmm a random effect. Alternatively, this model may be viewed ...

The Bayesian interpretation of probability is one of two broad categories of interpretations. Bayesian inference updates knowledge about unknowns, parameters, with information from data. The LaplacesDemon package in R enables Bayesian inference, and this vignette provides an introduction to the topic. This article introduces Bayes’ theorem, model-based Bayesian inference, components of Bayesian...

In current magnetic resonance imaging (“MRI”) systems, low-field MRI has the advantage of low cost and open. The signal-to-noise ratio (“SNR”) obtained, however, is relatively low. This study thus aims at developing a Bayesian multi-stage hierarchical model with an outlier-detection ability, through the use of a heavy-tailed prior that can be used to reduce the effects of noise introduced. Sinc...

RATIONALE The doubly labelled water (DLW) method is the reference method for the estimation of free-living total energy expenditure (TEE). In this method, where both 2 H and 18 O are employed, different approaches have been adopted to deal with the non-conformity observed regarding the distribution space for the labels being non-coincident with total body water. However, the method adopted can ...

Bayesian predictive inference analyzes a dataset to make predictions about new observations. When a model does notmatch the data, predictive accuracy suffers. We develop population empirical Bayes (pop-eb), a hierarchical framework that explicitly models the empirical population distribution as part of Bayesian analysis. We introduce a new concept, the latent dataset, as a hierarchical variable...

We present two new algorithms for fast Bayesian Hierarchical Clustering on large data sets. Bayesian Hierarchical Clustering (BHC) [1] is a method for agglomerative hierarchical clustering based on evaluating marginal likelihoods of a probabilistic model. BHC has several advantages over traditional distancebased agglomerative clustering algorithms. It defines a probabilistic model of the data a...

Bayesian predictive inference analyzes a dataset to make predictions about new observations. When a model does not match the data, predictive accuracy su ers. We develop population empirical Bayes ( ), a hierarchical framework that explicitly models the empirical population distribution as part of Bayesian analysis. We introduce a new concept, the latent dataset, as a hierarchical variable and ...

Bayesian Networks (BNs) are applied to a wide range of applications. In the past few years great interest is dedicated to the problem of inferring the structure of BNs solely from the data. In this work we explore a probabilistic method which enables the inclusion of extra knowledge in the inference of BNs. We briefly present the theory of BNs and introduce our probabilistic model. We also pres...