Bayesian Inference under Cluster Sampling with Probability Proportional to Size

Cluster sampling is common in survey practice, and the corresponding inference has been predominantly design-based. We develop a Bayesian framework for cluster sampling and account for the design effect in the outcome modeling. We consider a two-stage cluster sampling design where the clusters are first selected with probability proportional to cluster size, and then units are randomly sampled inside selected clusters. Challenges arise when the sizes of nonsampled cluster are unknown. We propose nonparametric and parametric Bayesian approaches for predicting the unknown cluster sizes, with this inference performed simultaneously with the model for survey outcome. Simulation studies show that the integrated Bayesian approach outperforms classical methods with efficiency gains. We use Stan for computing and apply the proposal to the Fragile Families and Child Wellbeing study as an illustration of complex survey inference in health surveys

Aggregate Selection in Evolutionary Robotics

Can the processes of natural evolution be mimicked to create robots or autonomous agents? This question embodies the most fundamental goals of evolutionary robotics (ER). ER is a field of research that explores the use of artificial evolution and evolutionary computing for learning of control in autonomous robots, and in autonomous agents in general. In a typical ER experiment, robots, or more precisely their control systems, are evolved to perform a given task in which they must interact dynamically with their environment. Controllers compete in the environment and are selected and propagated based on their ability (or fitness) to perform the desired task. A key component of this process is the manner in which the fitness of the evolving controllers is measured. In ER, fitness is measured by a fitness function or objective function. This function applies some given criteria to determine which robots or agents are better at performing the task for which they are being evolved. Fitness functions can introduce varying levels of a priori knowledge into evolving populations. Som

Tracking Referents

[Tsai88] R. Tsai, “Overview of a unified calibration trio for robot eye, eyeto-hand, and hand calibration using 3D machine vision, ” Research Report RC. International Business Machines Corporation, Research Division; RC 14218, 1988. [Tsai86] R. Tsai, “Review of the two-stage camera calibration technique plus some implementation tips and new techniques for center an

Interval estimators for the population mean for skewed distributions with a small sample size

In finite population sampling, it has long been known that, for small sample sizes, when sampling from a skewed population, the usual frequentist intervals for the population mean cover the true value less often than their stated frequency of coverage. Recently, a non-informative Bayesian approach to some problems in finite population sampling has been developed, which is based on the 'Polya posterior'. For large sample sizes, these methods often closely mimic standard frequentist methods. In this paper, a modification of the 'Polya posterior', which employs the weighted Polya distribution, is shown to give interval estimators with improved coverage properties for problems with skewed populations and small sample sizes. This approach also yields improved tests for hypotheses about the mean of a skewed distribution.

REAPER: A Reflexive Architecture For Perceptive Agents

This article describes the winning entries in the 2000 Association for the Advancement of Artificial Intelligence Mobile Robot Competition. The robots, developed by Swarthmore College, all used a modular hybrid architecture designed to enable reflexive responses to perceptual input. Within this architecture, the robots integrated visual sensing, speech synthesis and recognition, the display of an animated face, navigation, and interrobot communication. In the Hors d\u27Oeuvres, Anyone? event, a team of robots entertained the crowd while they interactively served cookies; and in the Urban Search-and-Rescue event, a single robot autonomously explored a section of the test area, identified interesting features, built an annotated map, and exited the test area within the allotted time