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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 28. Chapters: Bayesian inference, Calculating demand forecast accuracy, Consensus forecast, Data assimilation, Demand forecasting, Ensemble forecasting, Fan chart (time series), Forecast bias, Forecast skill, Futures techniques, Growth curve, Hindcast, International Futures, Meteorological reanalysis, Mixed data sampling, Political forecasting, Probabilistic forecasting, Probability of precipitation, Tolerance interval, Trend estimation. Excerpt: 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 learned. 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; however, non-Bayesian updating rules are compatible with rationality, according to philosophers Ian Hacking and Bas van Fraassen. Bayesian inference derives the posterior probability as a consequence of two antecedents, a prior probability and a "likelihood function" derived from a probability model for the data to be observed. Bayesian inference computes the posterior probability according to Bayes' rule: where Note that what affects the value of for different values of is only the factors and, which both appear in the...