John Wilkins - Frequentism vs Bayesianism скачать в хорошем качестве

John Wilkins - Frequentism vs Bayesianism

Frequentist inference is one of a number of possible techniques of formulating generally applicable schemes for making statistical inference: That implies of drawing conclusions from sample data by the emphasis on the frequency or proportion of the data. An alternative name is frequentist statistics. This is the inference framework in which the well-established methodologies of statistical hypothesis testing and confidence intervals are based. Other than frequentistic inference, the main alternative approach to statistical inference is Bayesian inference, while another is fiducial inference. While "Bayesian inference" is sometimes held to include the approach to inference leading to optimal decisions, a more restricted view is taken here for simplicity. -- In statistics, Bayesian inference is a method of inference in which Bayes' rule is used to update the probability estimate for a hypothesis as additional evidence is acquired. Bayesian updating is an important technique throughout statistics, and especially in mathematical statistics. For some cases, exhibiting a Bayesian derivation for a statistical method automatically ensures that the method works as well as any competing method. Bayesian updating is especially important in the dynamic analysis of a sequence of data. Bayesian inference has found application in a range of fields including science, engineering, philosophy, medicine and law. In the philosophy of decision theory, Bayesian inference is closely related to discussions of subjective probability, often called "Bayesian probability". Bayesian probability provides a rational method for updating beliefs. Subscribe to this Channel: http://youtube.com/subscription_cente... Science, Technology & the Future: http://scifuture.org Humanity+: http://humanityplus.org @john_s_wilkins #philsci