本书论述了自17世纪迄今数理统计学发展的简要历史。内容包括:概率基本概念的起源和发展,伯努利大数定律和狄莫旨二项概率正态逼近,贝叶斯关于统计推断的思想,最小二乘法与误差分布--高其正态分布的发现过程,社会统计学家对数理统计方法的主要贡献等。

因为精确预测以及在科技领域的广泛应用，量子力学被认为是最成功的科学理论之一，但也是最被误解的理论之一。在被创立后的近一个世纪，量子力学仍旧充满了争议。通过量子贝叶斯理论（QBism）解释量子理论中的悖论和谜题，本书为非专业的读者阐述了量子力学深远的含义、如何理解量子力学和量子力学如何与这个世界相互作用。QBism用对概率的全新理解去改造量子力学中的传统特征。贝叶斯概率与标准的“频率概率”不同的是，它是观察者对未来将要发生的一个事件或者一个命题的信任程度的数值测量。相比于频率主义，量子贝叶斯理论的优势在于它能够处理单个事件，它的概率估计可以根据获得的新信息去更新，并且可以包含“频率概率”的结果。但最重要的还是与量子理论相关的奇怪之处——如两个原子可以同时在不同的位置，信号可以传播得比光更快，以及薛定谔的猫可以同时处于死和活的状态的想法。
用直白的语言而不是方程，贝耶尔用一种通俗的方式，揭示了量子力学的意义，发现了认识物理学的新途径。

Causality is a key part of many fields and facets of life, from finding the relationship between diet and disease to discovering the reason for a particular stock market crash. Despite centuries of work in philosophy and decades of computational research, automated inference and explanation remains an open problem. In particular, the timing and complexity of relationships has been largely ignored even though this information is critically important for prediction, explanation and intervention. However, given the growing availability of large observational datasets including those from electronic health records and social networks, it is a practical necessity. This book presents a new approach to inference (finding relationships from a set of data) and explanation (assessing why a particular event occurred), addressing both the timing and complexity of relationships. The practical use of the method developed is illustrated through theoretical and experimental case studies, demonstrating its feasibility and success.

This book offers a detailed history of parametric statistical inference. Covering the period between James Bernoulli and R.A. Fisher, it examines: binomial statistical inference; statistical inference by inverse probability; the central limit theorem and linear minimum variance estimation by Laplace and Gauss; error theory, skew distributions, correlation, sampling distributions; and the Fisherian Revolution. Lively biographical sketches of many of the main characters are featured throughout, including Laplace, Gauss, Edgeworth, Fisher, and Karl Pearson. Also examined are the roles played by DeMoivre, James Bernoulli, and Lagrange.

This well-respected text is designed for the first course in probability and statistics taken by students majoring in Engineering and the Computing Sciences. The prerequisite is one year of calculus. The text offers a balanced presentation of applications and theory. The authors take care to develop the theoretical foundations for the statistical methods presented at a level that is accessible to students with only a calculus background. They explore the practical implications of the formal results to problem-solving so that students gain an understanding of the logic behind the techniques as well as practice in using them. The examples, exercises, and applications were chosen specifically for students in engineering and computer science, and include opportunities for real data analysis.

The first English translation of Hans Reichenbach's lucid doctoral thesis sheds new light on how Kant’s Critique of Pure Reason was understood in some quarters at the time. The source of several themes in his still influential The Direction of Time, the thesis shows Reichenbach's early focus on the interdependence of physics, probability, and epistemology.