The Weight of Evidence

Courts of law must weigh evidence to determine the likelihood of competing interpretations of past events, and different legal contexts require different standards of proof, but this falls short of a quantification of probability. Bayes’s theorem and the associated formula provide a way of combining multiple elements of evidence and using them to refine prior assessments of probability. The prosecutor’s fallacy involves an incorrect reversal of the logic of evidence. The ecological fallacy involves incorrectly attributing proportions derived from large groups to smaller groups or individuals.

Prediction and Assessment of the Occupational Risks in the Mining Industry Using the Bayess Theorem

The analysis of existing methods for assessing occupational risks is carried out, and the need for searchinga fundamentally new approach to the assessment and prediction of risks in the mining industry is substantiated. Based on the results of the analysis of modern methods and technologies, it is established that the development of the methodology for assessment and prediction of the occupational risks using Bayes's theorem has significant advantages: simplicity and accessibility for the occupational safety specialists, reproducibility considering many factors of working conditions, as well as the possibility of preventive measures prediction and development. The application of Bayes's theorem is promising in determining cause-and-effect relationships and predicting the occupational morbidity of the employees, which is also an advantage of this methodology for managing occupational risks in the mining industry. Bayes's approaches to modeling are characterized by high performance, intuitively clear in the form of a graph. The example is given concerning the application of Bayes's theorem to assess the risk of a fatal incident taking into account the statistics on the mining industry. Also, the simplest types of Bayes’s trust networks were developed reflecting the possibility of establishing cause-and-effect relationships (both for assessment and prediction), and are the basis for further modeling.

Evidence and Probability Data

This chapter discusses evidence and probability data with particular attention on Bayesian estimation. The Protestant ethic slowed probability developments in the United States, but the idea of quantification continued apace in England and on the Continent. In particular, Thomas Bayes invented a simple but profound mathematical means to connect outcomes with causes with conditional probabilities and Bayesian estimation. The chapter explains conditional probabilities and Bayesian logic, giving several examples, including incidence of accurate cancer diagnosis with inexact diagnostics. The chapter also introduces Bayes’s magnum opus An Essay Toward Solving a Problem in the Doctrine of Chances and gives his example of rolling billiard balls on a billiard table to show Bayes’s theorem.

Klasifikasi Penyebab Penyalahgunaan Narkoba Dari Berita Online Dengan Menggunakan Naive Bayes

This research conducted the classification process by applying the method of classification of Naive Bayes. News article document is one form of text data that is not structured so that requires the process of cleaning data and pre-processing first. The Naive Bayes approach is an approach that refers to Bayes's Theorem, where it uses the principle of statistical opportunity to combine previous knowledge. The use of this technique is based on the need of the system to know the probability value of the data to be classified. Waterfall method was used for built this classificaiton system. Accuracy rate up to 60 % with the highest precision and recall is 80% and 90%.