Twisted particle filters

We investigate sampling laws for particle algorithms and the influence of these laws on the efficiency of particle approximations of marginal likelihoods in hidden Markov models. Among a broad class of candidates we characterize the essentially unique family of particle system transition kernels which is optimal with respect to an asymptotic-in-time variance growth rate criterion. The sampling structure of the algorithm defined by these optimal transitions turns out to be only subtly different from standard algorithms and yet the fluctuation properties of the estimates it provides can be dramatically different. The structure of the optimal transition suggests a new class of algorithms, which we term "twisted" particle filters and which we validate with asymptotic analysis of a more traditional nature, in the regime where the number of particles tends to infinity.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1167 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

A Laboratory for Relevance: Findings and Recommendations from the Arts Innovation Fund

Starting in 2006, a group of leading California arts institutions set out to innovate with new ways of working in the 21st century. With support from the Arts Innovation Fund of The James Irvine Foundation, they approached the challenge of innovation in a variety of ways, with a wide range of objectives and results. Across the board, the experimentation process prompted organizational reflection and change. Most grantees developed new levels of adaptive capacity, an attribute that many thought leaders believe will be essential for arts organizations, and the arts sector as a whole, to thrive into the future. After a strategic qualitative review of the innovation projects pursued by organizations participating in the Arts Innovation Fund, the Slover Linett evaluation team offers the following report with its insights and recommendations

The Feasibility of Neuroimaging Methods in Marketing Research

On July 17, 1990, President George Bush issued “Proclamation #6158” which boldly declared the following ten years would be called the “Decade of the Brain” (Bush, 1990). Accordingly, the research mandates of all US federal biomedical institutions worldwide were redirected towards the study of the brain in general and cognitive neuroscience specifically. In 2008, one of the greatest legacies of this “Decade of the Brain” is the impressive array of techniques that can be used to study cortical activity. We now stand at a juncture where cognitive function can be mapped in the time, space and frequency domains, as and when such activity occurs. These advanced techniques have led to discoveries in many fields of research and clinical science, including psychology and psychiatry. Unfortunately, neuroscientific techniques have yet to be enthusiastically adopted by the social sciences. Market researchers, as specialized social scientists, have an unparalleled opportunity to adopt cognitive neuroscientific techniques and significantly redefine the field and possibly even cause substantial dislocations in business models. Following from this is a significant opportunity for more commercially-oriented researchers to employ such techniques in their own offerings. This report examines the feasibility of these techniques

The Brain in Business: The Case for Organisational Cognitive Neuroscience?

The application of cognitive neuroscientific techniques to understanding social behaviour has resulted in many discoveries. Yet advocates of the ‘social cognitive neuroscience’ approach maintain that it suffers from a number of limitations. The most notable of these is its distance from any form of real-world applicability. One solution to this limitation is ‘Organisational Cognitive Neuroscience’ – the study of the cognitive neuroscience of human behaviour in, and in response to, organizations. Given that all of us will spend most of our lives in some sort of work related organisation, organisational cognitive neuroscience allows us to examine the cognitive underpinnings of social behaviour that occurs in what may be our most natural ecology. Here we provide a brief overview of this approach, a definition and also some possible questions that the new approach would be best suited to address

Examining Role of Self-Control Exertion in the Strength Model of Self-Control Using Modified Versions of the Sequential Task Paradigm

Current study examines the role of effort on the ego-depletion effect in a sequential-task experimental paradigm as employed by the strength model of self-control. Evidence from three studies, including one meta-analysis and two laboratory experiments, indicates that conventional approaches of measuring self-control exertion is inadequate for testing the strength model. Furthermore, attempts to employ monetary incentives and task duration similarly did not have any significant effect on the ego-depletion effect

Global consensus Monte Carlo

To conduct Bayesian inference with large data sets, it is often convenient or necessary to distribute the data across multiple machines. We consider a likelihood function expressed as a product of terms, each associated with a subset of the data. Inspired by global variable consensus optimisation, we introduce an instrumental hierarchical model associating auxiliary statistical parameters with each term, which are conditionally independent given the top-level parameters. One of these top-level parameters controls the unconditional strength of association between the auxiliary parameters. This model leads to a distributed MCMC algorithm on an extended state space yielding approximations of posterior expectations. A trade-off between computational tractability and fidelity to the original model can be controlled by changing the association strength in the instrumental model. We further propose the use of a SMC sampler with a sequence of association strengths, allowing both the automatic determination of appropriate strengths and for a bias correction technique to be applied. In contrast to similar distributed Monte Carlo algorithms, this approach requires few distributional assumptions. The performance of the algorithms is illustrated with a number of simulated examples