Bayesian Model Averaging in R

Bayesian model averaging has increasingly witnessed applications across an array of empirical contexts. However, the dearth of available statistical software which allows one to engage in a model averaging exercise is limited. It is common for consumers of these methods to develop their own code, which has obvious appeal. However, canned statistical software can ameliorate one's own analysis if they are not intimately familiar with the nuances of computer coding. Moreover, many researchers would prefer user ready software to mitigate the inevitable time costs that arise when hard coding an econometric estimator. To that end, this paper describes the relative merits and attractiveness of several competing packages in the statistical environment R to implement a Bayesian model averaging exercise.Model Averaging, Zellner's g Prior, BMS

A Review of the `BMS' Package for R

This paper describes the relative merits and attractiveness of the newest Bayesian model averaging package, BMS, available in the statistical software R to implement a Bayesian model averaging exercise. This package provides the user with a wide range of customizable priors for conducting a BMA analysis, provides ample graphs to visualize the results and offers several alternative model search mechanisms.Model Averaging, Zellner's g Prior, BMS

Decomposing The Conditional Variance of Cross-Country Output

A well established fact in the growth empirics literature is the increasing variation in output per capita across countries. This phenomena however does not adequately describe changes in the distribution of output since it does not account for changes in the covariates which undoubtedly in influence per capita output levels. We propose a robust, nonparametric decomposition of the conditional variation of per capita output and find that OECD countries have experienced diminishing conditional variation while other regions have experienced increasing conditional variation. Our decomposition suggests that most of these changes in the conditional variance of output is due to unobserved factors not accounted for by the traditional growth determinants. In addition to this we show as these factors played very different roles over time and across regions.Generalized Kernel, Nonparametric, Conditional Variation

A Multi-Agent Architecture for Distributed Domain-Specific Information Integration

On both the public Internet and private Intranets, there is a vast amount of data available that is owned and maintained by different organizations, distributed all around the world. These data resources are rich and recent; however, information gathering and knowledge discovery from them, in a particular knowledge domain, confronts major difficulties. The objective of this article is to introduce an autonomous methodology to provide for domain-specific information gathering and integration from multiple distributed sources

Low-threshold heterogeneously integrated InP/SOI lasers with a double adiabatic taper coupler

We report on a heterogeneously integrated InP/silicon-on-insulator (SOI) laser source realized through divinylsiloxane-bis-benzocyclobutene (DVS-BCB) wafer bonding. The hybrid lasers present several new features. The III-V waveguide has a width of only 1.7 mu m, reducing the power consumption of the device. The silicon waveguide thickness is 400 nm, compatible with high-performance modulator designs and allowing efficient coupling to a standard 220-nm high index contrast silicon waveguide layer. In order to make the mode coupling efficient, both the III-V waveguide and silicon waveguide are tapered, with a tip width for the III-V waveguide of around 800 nm. These new features lead to good laser performance: a lasing threshold as low as 30 mA and an output power of more than 4 mW at room temperature in continuous-wave operation regime. Continuous wave lasing up to 70 degrees C is obtained

Interactive Segmentation in Multimodal Medical Imagery Using a Bayesian Transductive Learning Approach

Labeled training data in the medical domain is rare and expensive to obtain. The lack of labeled multimodal medical image data is a major obstacle for devising learning-based interactive segmentation tools. Transductive learning (TL) or semi-supervised learning (SSL) offers a workaround by leveraging unlabeled and labeled data to infer labels for the test set given a small portion of label information. In this paper we propose a novel algorithm for interactive segmentation using transductive learning and inference in conditional mixture nave Bayes models (T-CMNB) with spatial regularization constraints. T-CMNB is an extension of the transductive nave Bayes algorithm [1, 20]. The multimodal Gaussian mixture assumption on the class-conditional likelihood and spatial regularization constraints allow us to explain more complex distributions required for spatial classification in multimodal imagery. To simplify the estimation we reduce the parameter space by assuming nave conditional independence between the feature space and the class label. The nave conditional independence assumption allows efficient inference of marginal and conditional distributions for large scale learning and inference [19]. We evaluate the proposed algorithm on multimodal MRI brain imagery using ROC statistics and provide preliminary results. The algorithm shows promising segmentation performance with a sensitivity and specificity of 90.37% and 99.74% respectively and compares competitively to alternative interactive segmentation schemes

Bayesian transduction and Markov conditional mixtures for spatiotemporal interactive segmentation

In this paper we propose a novel transductive learning machine for spatiotemporal classification casted as an interactive segmentation problem. We present Markov conditional mixtures of naive Bayes models with spatiotemporal regularization constraints in a transductive learning and inference framework. The proposed model extends on previous work to account for non independent and identically distributed (i.i.d.) sequential data by imposing the learning and inference problem w.r.t. time. The multimodal mixture assumption on the class-conditional likelihood for each covariate feature domain in conjunction with spatiotemporal regularization constraints allow us to explain more complex distributions required for classification in multimodal longitudinal brain imagery. We evaluate the proposed algorithm on multimodal temporal MRI brain images using ROC statistics and report preliminary results