Challenges to law at the end of the 20th Century.- v.3.

In The Second Jewish Revolt: The Bar Kokhba War, 132-136 C.E., Menahem Mor offers a detailed account on the Bar Kokhba Revolt in an attempt to understand the second revolt against the Romans. Since the Bar Kokhba Revolt did not have a historian who devoted a comprehensive book to the event, Mor used a variety of historical materials including literary sources (Jewish, Christian, Greek and Latin) and archaeological sources (inscriptions, coins, military diplomas, hideouts, and refuge complexes). The book reviews the causes for the outbreak while explaining the complexity of the territorial expansion of the Revolt. Mor portrays the participants and opponents as well as the attitudes of the non-Jewish population in Palestine. He exposes the Roman Army’s part in Judaea, the Jewish leadership and the implications of the Revolt.

Computational Learning Theory presents the theoretical issues in machine learning and computational models of learning. This book covers a wide range of problems in concept learning, inductive inference, and pattern recognition. Organized into three parts encompassing 32 chapters, this book begins with an overview of the inductive principle based on weak convergence of probability measures. This text then examines the framework for constructing learning algorithms. Other chapters consider the formal theory of learning, which is learning in the sense of improving computational efficiency as opposed to concept learning. This book discusses as well the informed parsimonious (IP) inference that generalizes the compatibility and weighted parsimony techniques, which are most commonly applied in biology. The final chapter deals with the construction of prediction algorithms in a situation in which a learner faces a sequence of trials, with a prediction to be given in each and the goal of the learner is to make some mistakes. This book is a valuable resource for students and teachers.

Both in science and in practical affairs we reason by combining facts only inconclusively supported by evidence. Building on an abstract understanding of this process of combination, this book constructs a new theory of epistemic probability. The theory draws on the work of A. P. Dempster but diverges from Depster's viewpoint by identifying his "lower probabilities" as epistemic probabilities and taking his rule for combining "upper and lower probabilities" as fundamental. The book opens with a critique of the well-known Bayesian theory of epistemic probability. It then proceeds to develop an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions. This rule, together with the idea of "weights of evidence," leads to both an extensive new theory and a better understanding of the Bayesian theory. The book concludes with a brief treatment of statistical inference and a discussion of the limitations of epistemic probability. Appendices contain mathematical proofs, which are relatively elementary and seldom depend on mathematics more advanced that the binomial theorem.