Bayesian Nonparametric Hidden Semi-Markov Models

There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the ubiquitous Hidden Markov Model for learning from sequential and time-series data. However, in many settings the HDP-HMM's strict Markovian constraints are undesirable, particularly if we wish to learn or encode non-geometric state durations. We can extend the HDP-HMM to capture such structure by drawing upon explicit-duration semi-Markovianity, which has been developed mainly in the parametric frequentist setting, to allow construction of highly interpretable models that admit natural prior information on state durations. In this paper we introduce the explicit-duration Hierarchical Dirichlet Process Hidden semi-Markov Model (HDP-HSMM) and develop sampling algorithms for efficient posterior inference. The methods we introduce also provide new methods for sampling inference in the finite Bayesian HSMM. Our modular Gibbs sampling methods can be embedded in samplers for larger hierarchical Bayesian models, adding semi-Markov chain modeling as another tool in the Bayesian inference toolbox. We demonstrate the utility of the HDP-HSMM and our inference methods on both synthetic and real experiments

Dirichlet Posterior Sampling with Truncated Multinomial Likelihoods

We consider the problem of drawing samples from posterior distributions formed under a Dirichlet prior and a truncated multinomial likelihood, by which we mean a Multinomial likelihood function where we condition on one or more counts being zero a priori. Sampling this posterior distribution is of interest in inference algorithms for hierarchical Bayesian models based on the Dirichlet distribution or the Dirichlet process, particularly Gibbs sampling algorithms for the Hierarchical Dirichlet Process Hidden Semi-Markov Model. We provide a data augmentation sampling algorithm that is easy to implement, fast both to mix and to execute, and easily scalable to many dimensions. We demonstrate the algorithm's advantages over a generic Metropolis-Hastings sampling algorithm in several numerical experiments

Computer‐based interactive tutorial versus traditional lecture for teaching introductory aspects of pain

In the health sciences, a wide range of computer‐based courseware is now available. The aim of the study described in this paper has been to compare the effectiveness of a computer‐based learning (CBL) software package and a traditional lecture (TL) for the delivery, of introductory material on pain. Nineteen undergraduate nursing students were divided into two groups to attend a one‐hour learning session which introduced clinical aspects of pain and which was delivered by either CBL or TL. Students were assessed for prior knowledge by a pre‐session test, and for knowledge gain by an identical post‐session test. In addition, a multiple‐choice question paper was used to examine differences in pain knowledge between the two groups, and a questionnaire was used to examine the students’ views on their experience during the learning session. The results demonstrated that both groups showed significant knowledge gain after their respective learning sessions. No significant differences between the groups in the magnitude of knowledge gain were found for clinical aspects of pain delivered during the learning sessions. The attitude questionnaire revealed that students attending CBL reported similar learning experiences to those attending the lecture

Physiological Studies of Heat Stress Acclimation During a Specific Exercise Regimen

Eleven subjects were used to determine if the exercise regimen of racquetball could be used as a heat stress acclimator. Core temperature, skin temperature, sweat production, and weight loss were recorded during a racquetball match. Skin and core temperatures were determined by using thermistors. Sweat was collected with modified stress electrodes. Weight loss was recorded by comparing nude weights at the beginning and end of a match. The results indicated that an hour of strenuous racquetball play caused a significant increase in core temperature with subsequent sweating which resulted in a significant decrease in skin temperature and weight loss. The exercise regimen of racquetball can act as a good heat stress acclimator because it produces sufficiently high levels of hyperthermia

Review of Reading Theory Now: An ABC of Good Reading with J. Hillis Miller, by Éamonn Dunne

Review of Reading Theory Now: An ABC of Good Reading with J. Hillis Miller, by Éamonn Dunn

Lagrangian Relaxation for MAP Estimation in Graphical Models

We develop a general framework for MAP estimation in discrete and Gaussian graphical models using Lagrangian relaxation techniques. The key idea is to reformulate an intractable estimation problem as one defined on a more tractable graph, but subject to additional constraints. Relaxing these constraints gives a tractable dual problem, one defined by a thin graph, which is then optimized by an iterative procedure. When this iterative optimization leads to a consistent estimate, one which also satisfies the constraints, then it corresponds to an optimal MAP estimate of the original model. Otherwise there is a ``duality gap'', and we obtain a bound on the optimal solution. Thus, our approach combines convex optimization with dynamic programming techniques applicable for thin graphs. The popular tree-reweighted max-product (TRMP) method may be seen as solving a particular class of such relaxations, where the intractable graph is relaxed to a set of spanning trees. We also consider relaxations to a set of small induced subgraphs, thin subgraphs (e.g. loops), and a connected tree obtained by ``unwinding'' cycles. In addition, we propose a new class of multiscale relaxations that introduce ``summary'' variables. The potential benefits of such generalizations include: reducing or eliminating the ``duality gap'' in hard problems, reducing the number or Lagrange multipliers in the dual problem, and accelerating convergence of the iterative optimization procedure.Comment: 10 pages, presented at 45th Allerton conference on communication, control and computing, to appear in proceeding