An essay towards solving a problem in the doctrine of chances

2003

Citations in the corpus

• Interactive machine learning for health informatics: when do we need the human-in-the-loop? (2016, background): “The theoretical basis for the probabilistic ML was laid by Thomas Bayes (1701–1761), [4, 5].”
• GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs (2018, methodology): “Following Bayes’ theorem [221, 222], the posterior is proportional to the product of the likelihood of the”
• Planning algorithms (2006, methodology): “A Bayesian classifier can be designed to maximize the probability of success.”
• Harold Jeffreys’s Theory of Probability Revisited (2008, background): “Bayes’ theorem is defined as the principle of inverse probability and only for finite sets, rather than for measures.7 Obviously, the general version of Bayes’ theorem is used in the sequel for continuous parameter spaces. Section 1.3 represents one of the few forays of the book into the realm of de”
• Opcode sequences as representation of executables for data-mining-based unknown malware detection (2013, background): “Bayes’ Theorem [2] is the basis of the so-called Bayesian inference, a statistical reasoning method that determines, based on a number of observations, the probability that a hypothesis may be true.”