Shaping the Landscape with Landmark Advocacy

For Stan Herr, public service is not something to do on the side

Michael Millemann: Putting Maryland\u27s Legal Clinics on the Map

Co-Director Michael Millemann\u27s tireless work has gained national prominence for the law school\u27s Clinical Law Program

Bayesian classification theory

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework and using various mathematical and algorithmic approximations, the AutoClass system searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit or share model parameters though a class hierarchy. We summarize the mathematical foundations of AutoClass

Analysis of selection pressure exerted on Plasmopara viticola by organically based fungicides

Downy mildew is one of the most important grape diseases world-wide. The pathogen is a genetically highly diversified organism with a high capacity of adaptation. A monitoring of changes in population structure of P. viticola subjected to new copper replacing products or strategies, studied and developed within REPCO (Replacement of Copper Fungicides in Organic Production of Grapevine and Apple in Europe) is important for assessing selection pressure which could lead to a reduction of efficacy of these new measures. Therefore P. viticola lesions collected on untreated and treated vines were analyzed by means of microsatellite markers. No significant differences in the populations structure were determined among untreated and treated populations, indicating that the applied products didn’t exerted any selection pressure on the P. viticola populations

Autoclass: An automatic classification system

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the AutoClass System searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit, or share, model parameters through a class hierarchy. The mathematical foundations of AutoClass are summarized

Market Allocation in the Health Insurance Industry and the McCarran- Ferguson Act

30 p.This Article examines the scope of the McCarran-Ferguson Act under existing Supreme Court precedent and reviews the sparse case law addressing the MFA’s applicability to market allocation schemes in the insurance industry, including the Blue Cross Blue Shield market allocation scheme. This Article concludes that whether any market allocation scheme is exempt is a close, fact-specific question that courts will not answer in the abstract. On any set of facts, insurers will have considerable leeway in attempting to prove that a given market allocation scheme should be treated as the business of insurance and thus exempt if regulated by state law. A clear determination that the BCBS market allocation scheme is not exempt, or congressional action to repeal the MFA as to the health insurance industry, would remove a primary obstacle to a challenge of the scheme, but it is not clear whether this would affect competitive dynamics among BCBS companies