Distance Estimation in Cosmology

In this paper we outline the framework of mathematical statistics with which one may study the properties of galaxy distance estimators. We describe, within this framework, how one may formulate the problem of distance estimation as a Bayesian inference problem, and highlight the crucial question of how one incorporates prior information in this approach. We contrast the Bayesian approach with the classical `frequentist' treatment of parameter estimation, and illustrate -- with the simple example of estimating the distance to a single galaxy in a redshift survey -- how one can obtain a significantly different result in the two cases. We also examine some examples of a Bayesian treatment of distance estimation -- involving the definition of Malmquist corrections -- which have been applied in recent literature, and discuss the validity of the assumptions on which such treatments have been based.Comment: Plain Latex version 3.1, 18 pages + 2 figures, `Vistas in Astronomy' in pres

Trailing Device for Tractors and the Like

Patent for a device built to connect behind tractors and other farming vehicles. Development and use are within text; includes illustrations to demonstrate the object of the patent

On Bayesian Statistics in Astronomical Investigation - Source Detection with Low Particle Counts

This paper is largely concerned with cameo examples designed to communicate this precept to astronomers interested and involved in statistical work in their investigations. I like the paper, and have a great deal of respect for the entrepreneurial efforts of the author and his co-authors to shift the focus of statistical analysis in the field toward the Bayesian paradigm. That these efforts have been rewarded is clear from browsing some of the referenced articles, notably the works of Loredo and Lamb (referenced in text). Here we find advanced physical and statistical models subjected to formal and rigorous Bayesian analysis, some requiring high dimensional numerical integrations performed via Monte Carlo, that yield inferences in terms of posterior distributions for parameters of interest that clearly and unambiguously address detailed and substantive scientific issues. A reading of these works provides a clear picture of investigators led to adherence to the Bayesian paradigm on pragmatic and empirical grounds --- the Bayesian approach gets the (right) job done where all others fail. The current paper, by comparison, focuses on elementary examples to clearly identify difficulties and inconsistencies, both conceptual and technical, inherent in traditional inferential paradigms, and zealously argues for the Bayesian approach as the preferred alternative. I agree with much of what Loredo has written here. The Scientific Organising Committee exhorted invited discussants t