Optimal proposal distributions and adaptive MCMC

Abstract. We review recent work concerning optimal proposal scalings for Metropolis-Hastings MCMC algorithms, and adaptive MCMC algorithms for trying to improve the algorithm on the fly. 1. Introduction. The Metropolis-Hastings algorithm (Metropolis et al., 1953; Hastings, 1970) requires choice of proposal distributions, and it is well-known that some proposals work much better than others. Determining which proposal is best for a particular target distribution is both very important and very difficult. Often this problem is attacked in an ad hoc manne

COMPETENCY-BASED TRAINING IN NURSING: LIMITS AND POSSIBILITIES

Objective To analyze the possibilities and limits of competency-based training in nursing. Method An integrative review of the literature on the subject was carried out, and an analysis was made of the results of a survey evaluating a nursing course based on areas of competency. A dialog was then established between the review and the results of the research. Results On the question of which theoretical type of competency the articles from the literature relate to, there is a predominance of the constructivist perspective, followed by the functionalist approach and the dialog-based approach. In the dialog between the literature and the research, limits and possibilities were observed in the development of a training by areas of competency. Conclusion The dialog-based approach to competency is the proposition that most approximates to the profile defined by the National Curriculum Guidelines for training in nursing, and this was also identified in the evaluation survey that was studied. However, it is found that there are aspects on better work is needed, such as: partnership between school and the workplace, the role of the teacher, the role of the student, and the process of evaluation

CS281B/Stat241B: Advanced Topics in Learning Decision Making

Introduction to Kernel Methods Lecturer: Michael I. Jordan Scribes: Andrea Frome 1 Reversible Jump MCMC Returning to the mixture model at the end of last lecture (Figure 1), we could try to find P (k|y) by running the Gibbs sampler. This would require moralizing the graph (Figure 2), and at each sampling step, set the value of a node given the values of the nodes in its Markov blanket. We already have one problem in that we have continuous and discrete nodes, and we don't have a way to integrate over the continuous nodes. Another problem is that the number of components, k, can change, and the number of parameters changes with k. For fixed k, we could calculate P (k|y) and compare models for di#erent values of k, but we cannot move between models using Gibbs sampler. In contrast to this approach, reversible jump MCMC seeks to calculate the probability of all nodes in the graph by transitioning between models of di#erent k values as it samples. Review: Metropolis-Hastings (Chapte