Going beyond traditional textbook topics, 'A Modern Course in Statistical Physics' incorporates contemporary research in a basic course on statistical mechanics. From the universal nature of matter to the latest results in the spectral properties of decay processes, this book emphasizes the theoretical foundations derived from thermodynamics and probability theory underlying al...(展开全部)

查尔斯·弗朗西斯·霍凯特（Charles Francis Hockett,1916--2000），美国语言学家。1916年1月17日出生于美国俄亥俄州哥伦布市。1932年进入俄亥俄州立大学，硕士毕业后进入耶鲁大学攻读博士学位，在萨丕尔等著名学者的指导下进修人类学和语言学，同时经常到哥伦比亚大学参加鲍厄斯主持的美洲印第安语讨论会，1939年获人类学博士学位。1940-1942年期间，他继续从事学术研究，进行语言调查，并执教于密歇根大学，帮助弗里斯做研究工作。1942年应征入伍，在军队里做语言工作，编写供军队使用的汉语教材和词典。从1946年起，霍凯特一直在康奈尔大学教语言学和人类学，并曾主持基础汉语教学，为美国培养了一代语言学家和人类学家。1964年他曾担任美国语言学会会长。1973年被选入美国文理科学院，1974年又被选入国家科学院。1986年他曾来...(展开全部)

Python was recently ranked as today's most popular programming language on the TIOBE index, thanks to its broad applicability to design and prototyping to testing, deployment, and maintenance. With this updated fourth edition, you'll learn how to get the most out of Python, whether you're a professional programmer or someone who needs this language to solve problems in a partic...(展开全部)

F.瑞夫 （F.Reif），1927年出生于澳大利亚，1953年毕业于哈佛大学，获物理学博士学位。1960-1989年在加州大学伯克利分校物理系任教。除本书外，其另一本代表作Fundamentals of Statistical and Thermal Physics 也深受广大读者欢迎，至今仍广为流传。

A self-contained introduction to probability, exchangeability and Bayes' rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation...(展开全部)

This book provides a compact self-contained introduction to the theory and application of Bayesian statistical methods. The book is accessible to readers having a basic familiarity with probability, yet allows more advanced readers to quickly grasp the principles underlying Bayesian theory and methods. The examples and computer code allow the reader to understand and implement ...(展开全部)

A self-contained introduction to probability, exchangeability and Bayes' rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation...(展开全部)