bridgesampling: An R Package for Estimating Normalizing Constants

Statistical procedures such as Bayes factor model selection and Bayesian model averaging require the computation of normalizing constants (e.g., marginal likelihoods). These normalizing constants are notoriously difficult to obtain, as they usually involve high-dimensional integrals that cannot be solved analytically. Here we introduce an R package that uses bridge sampling (Meng & Wong, 1996; Meng & Schilling, 2002) to estimate normalizing constants in a generic and easy-to-use fashion. For models implemented in Stan, the estimation procedure is automatic. We illustrate the functionality of the package with three examples

Normalizers of Operator Algebras and Reflexivity

The set of normalizers between von Neumann (or, more generally, reflexive) algebras A and B, (that is, the set of all operators x such that xAx* is a subset of B and x*Bx is a subset of A) possesses `local linear structure': it is a union of reflexive linear spaces. These spaces belong to the interesting class of normalizing linear spaces, namely, those linear spaces U for which UU*U is a subset of U. Such a space is reflexive whenever it is ultraweakly closed, and then it is of the form U={x:xp=h(p)x, for all p in P}, where P is a set of projections and h a certain map defined on P. A normalizing space consists of normalizers between appropriate von Neumann algebras A and B. Necessary and sufficient conditions are found for a normalizing space to consist of normalizers between two reflexive algebras. Normalizing spaces which are bimodules over maximal abelian selfadjoint algebras consist of operators `supported' on sets of the form [f=g] where f and g are appropriate Borel functions. They also satisfy spectral synthesis in the sense of Arveson.Comment: 20 pages; to appear in the Proceedings of the London Mathematical Societ

Normalizing Rejection

Getting turned down for grant funding or having a manuscript rejected is an uncomfortable but not unusual occurrence during the course of a nurse researcher’s professional life. Rejection can evoke an emotional response akin to the grieving process that can slow or even undermine productivity. Only by “normalizing” rejection, that is, by accepting it as an integral part of the scientific process, can researchers more quickly overcome negative emotions and instead use rejection to refine and advance their scientific programs. This article provides practical advice for coming to emotional terms with rejection and delineates methods for working constructively to address reviewer comments

Normalizing or not normalizing? An open question for floating-point arithmetic in embedded systems

Emerging embedded applications lack of a specific standard when they require floating-point arithmetic. In this situation they use the IEEE-754 standard or ad hoc variations of it. However, this standard was not designed for this purpose. This paper aims to open a debate to define a new extension of the standard to cover embedded applications. In this work, we only focus on the impact of not performing normalization. We show how eliminating the condition of normalized numbers, implementation costs can be dramatically reduced, at the expense of a moderate loss of accuracy. Several architectures to implement addition and multiplication for non-normalized numbers are proposed and analyzed. We show that a combined architecture (adder-multiplier) can halve the area and power consumption of its counterpart IEEE-754 architecture. This saving comes at the cost of reducing an average of about 10 dBs the Signal-to-Noise Ratio for the tested algorithms. We think these results should encourage researchers to perform further investigation in this issue.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Asymptotically almost all \lambda-terms are strongly normalizing

We present quantitative analysis of various (syntactic and behavioral) properties of random \lambda-terms. Our main results are that asymptotically all the terms are strongly normalizing and that any fixed closed term almost never appears in a random term. Surprisingly, in combinatory logic (the translation of the \lambda-calculus into combinators), the result is exactly opposite. We show that almost all terms are not strongly normalizing. This is due to the fact that any fixed combinator almost always appears in a random combinator