Providing complete syllabus support (9709), this stretching and practice-focused course builds the advanced skills needed for the latest Cambridge assessments and the transition to higher education. Engaging, real world examples make mathematics relevant to real life.

Providing complete syllabus support (9709), this stretching and practice-focused course builds the advanced skills needed for the latest Cambridge assessments and the transition to higher education. Engaging, real world examples make mathematics relevant to real life.

The New Walford highlights the best resources to use when undertaking a search for accurate and relevant information, saving you precious time and effort. For those looking for a selective and evaluative reference resource that really delivers on its promise, look no further. In addition to print sources, The New Walford naturally covers an extensive range of e-reference sources such as digital databanks, digital reference services, electronic journal collections, meta-search engines, networked information services, open archives, resource discovery services and websites of premier organizations in both the public and private sectors. But rather than supplying a list of all available known resources as a web search engine might, The New Walford subject specialists have carefully selected and evaluated available resources to provide a definitive list of the most appropriate and useful. With an emphasis on quality and sustainability, the subject specialists have been careful to assess the differing ways that information is framed and communicated in different subject areas. As a result the resource evaluations in each subject area are prefaced by an introductory overview of the structure of the relevant literature. This ensures that The New Walford is clear, easy-to-use and intuitive. - Publisher.

The first six chapters of this volume present the author's 'predictive' or information theoretic' approach to statistical mechanics, in which the basic probability distributions over microstates are obtained as distributions of maximum entropy (Le. , as distributions that are most non-committal with regard to missing information among all those satisfying the macroscopically given constraints). There is then no need to make additional assumptions of ergodicity or metric transitivity; the theory proceeds entirely by inference from macroscopic measurements and the underlying dynamical assumptions. Moreover, the method of maximizing the entropy is completely general and applies, in particular, to irreversible processes as well as to reversible ones. The next three chapters provide a broader framework - at once Bayesian and objective - for maximum entropy inference. The basic principles of inference, including the usual axioms of probability, are seen to rest on nothing more than requirements of consistency, above all, the requirement that in two problems where we have the same information we must assign the same probabilities. Thus, statistical mechanics is viewed as a branch of a general theory of inference, and the latter as an extension of the ordinary logic of consistency. Those who are familiar with the literature of statistics and statistical mechanics will recognize in both of these steps a genuine 'scientific revolution' - a complete reversal of earlier conceptions - and one of no small significance.