Each year the JHEPS lists the books and articles on the history of probability and statistics that have appeared in the previous year. The list of 2007 publications is due to appear in February 2008. Of course, omissions can be made good at any time and, if you know of any in the list below, please contact me, John Aldrich
On April 25, 1903, Soviet mathematician Andrey Nikolaevich Kolmogorov was born. He was one of the most important mathematicians of the 20th century, who advanced various scientific fields, among them probability theory, topology, intuitionistic logic, turbulence, classical mechanics, algorithmic information theory and computational complexity.
On September 24, 1501, Italian Renaissance mathematician, physician, astrologer and gambler Gerolamo Cardano was born. He wrote more than 200 works on medicine, mathematics, physics, philosophy, religion, and music. But, he is best known for his gambling that led him to formulate elementary rules in probability, making him one of the founders of probability theory.
This course covers the design and analysis of randomized algorithms and, more generally, applications of randomness in computing. You will learn fundamental tools from probability and see many applications of randomness in computing.
Jakob Bernoulli, born in 1654, is best known for his work Ars Conjectandi (The Art of Conjecture), where he described the known results in probability theory and in enumeration, including the application of probability theory to games of chance.
On April 17, 1761, English mathematician and Presbyterian minister Thomas Bayes passed away. He is best known as name giver of the Bayes' theorem, of which he had developed a special case. T expresses (in the Bayesian interpretation) how a subjective degree of belief should rationally change to account for evidence, and finds application in in fields including science, engineering, economics (particularly microeconomics), game theory, medicine and law.
The Probability Web is a collection of probability resources on the World Wide Web (WWW). The pages are designed to be especially helpful to researchers, teachers, and people in the probability community.
Gaussian perspective of the world = built on atomism, privileging stability over instability, structure over process, objects over fields, and being over becoming. Paretian world = much more dynamic view of the world; looks for patterns in evolving relati
The EDRL research group works around a theoretical strain (embodied cognition), a methodological line (design-based research), and a disciplinary emphasis (mathematics). Thus, the laboratory hosts the full cycle of design-research projects that are geared to contribute to theory and practice of multi-modal mathematical learning and reasoning as well as to design theory.
In order to better understand complex Belief-Propagation models tested with our simulator we have identified a strong need for a simple visualization tool that will grant us insight of the tested graphs.
Web site for statistical computation; probability; linear correlation and regression; chi-square; t-procedures; t-tests; analysis of variance; ANOVA; analysis of covariance; ANCOVA; parametric; nonparametric; binomial; normal distribution; Poisson distribution; Fisher exact; Mann-Whitney; Wilcoxon; Kruskal-Wallis; Richard Lowry, Vassar College
Advances in Pure Mathematics (APM) is an international journal dedicated to the latest advancement of ordered algebraic structures. The goal of this journal is to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in different areas of ordered algebraic structures.
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers - An introduction to Bayesian methods + probabilistic programming in data analysis with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
- Robust and stochastic optimization
- Convex analysis
- Linear programming
- Monte Carlo simulation
- Model-based estimation
- Matrix algebra review
- Probability and statistics basics
Turning procedural and structural knowledge into programs has established methodologies, but what about turning knowledge into probabilistic models? I explore a few examples of what such a process could look like.
S. Doria. Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, volume 103 of Proceedings of Machine Learning Research, page 159--166. Thagaste, Ghent, Belgium, PMLR, (03--06 Jul 2019)
J. De Bock, and G. de Cooman. Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, volume 103 of Proceedings of Machine Learning Research, page 125--134. Thagaste, Ghent, Belgium, PMLR, (03--06 Jul 2019)