A Simple Class of Bayesian Nonparametric Autoregression Models

We introduce a model for a time series of continuous outcomes, that can be expressed as fully nonparametric regression or density regression on lagged terms. The model is based on a dependent Dirichlet process prior on a family of random probability measures indexed by the lagged covariates. The approach is also extended to sequences of binary responses. We discuss implementation and applications of the models to a sequence of waiting times between eruptions of the Old Faithful Geyser, and to a dataset consisting of sequences of recurrence indicators for tumors in the bladder of several patients.MIUR 2008MK3AFZFONDECYT 1100010NIH/NCI R01CA075981Mathematic

Interactive Extraction of High-Frequency Aesthetically-Coherent Colormaps

Color transfer functions (i.e. colormaps) exhibiting a high frequency luminosity component have proven to be useful in the visualization of data where feature detection or iso-contours recognition is essential. Having these colormaps also display a wide range of color and an aesthetically pleasing composition holds the potential to further aid image understanding and analysis. However producing such colormaps in an efficient manner with current colormap creation tools is difficult. We hereby demonstrate an interactive technique for extracting colormaps from artwork and pictures. We show how the rich and careful color design and dynamic luminance range of an existing image can be gracefully captured in a colormap and be utilized effectively in the exploration of complex datasets

Neo-Statecraft Theory, Historical Institutionalism and Institutional Change

This article provides a critical examination of the contribution that statecraft theory, which has been subject to recent revision and development, makes to the literature on institutional change. It articulates an emergent neo-statecraft approach that offers an agent-led form of historical institutionalism. This overcomes the common criticism that historical institutionalists underplay the creative role of actors. The article also argues that the approach brings back into focus the imperatives of electoral politics as a source of institutional change and provides a macro theory of change which is also commonly missing from historical institutionalist work. It can therefore identify previously unnoticed sources of stability and change, especially in states with strong executives and top-down political cultures

(WP 2016-05) Hodgson, Cumulative Causation, and Reflexive Economic Agents

This paper examines Geoff Hodgson’s interpretation of Veblen in agency-structure terms, and argues it produces a conception of reflexive economic agents. It then sets out an account of cumulative causation processes using this reflexive agent conception, modeling them as a two-part causal process, one part involving a linear causal relation and one part involving a circular causal relation. The paper compares the reflexive agent conception to the standard expected utility conception of economic agents, and argues that on a cumulative causation view of the world the completeness assumption essential to the standard view of rationality cannot be applied. The final discussion addresses the nature of the choice behavior of reflexive economic agents, using the thinking of Amartya Sen and Herbert Simon to frame how agents might approach choice in regard to each of the two different parts of cumulative causal processes, and closing with brief comments on behavioral economics’ understanding of reference dependence and position adjustment

Unsupervised Spoken Term Detection with Spoken Queries by Multi-level Acoustic Patterns with Varying Model Granularity

This paper presents a new approach for unsupervised Spoken Term Detection with spoken queries using multiple sets of acoustic patterns automatically discovered from the target corpus. The different pattern HMM configurations(number of states per model, number of distinct models, number of Gaussians per state)form a three-dimensional model granularity space. Different sets of acoustic patterns automatically discovered on different points properly distributed over this three-dimensional space are complementary to one another, thus can jointly capture the characteristics of the spoken terms. By representing the spoken content and spoken query as sequences of acoustic patterns, a series of approaches for matching the pattern index sequences while considering the signal variations are developed. In this way, not only the on-line computation load can be reduced, but the signal distributions caused by different speakers and acoustic conditions can be reasonably taken care of. The results indicate that this approach significantly outperformed the unsupervised feature-based DTW baseline by 16.16\% in mean average precision on the TIMIT corpus.Comment: Accepted by ICASSP 201