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Deterministic vs. probabilistic (stochastic). A deterministic model is one in which every set of variable states is uniquely determined by parameters in the model and by sets of previous states of these variables; therefore, a deterministic model always performs the same way for a given set of initial conditions.
This perspective is known as superdeterminism, and is defended by some physicists such as Sabine Hossenfelder and Tim Palmer. [ 95 ] More advanced variations on these arguments include quantum contextuality , by Bell, Simon B. Kochen and Ernst Specker , which argues that hidden variable theories cannot be "sensible", meaning that the values of ...
John Venn, who provided a thorough exposition of frequentist probability in his book, The Logic of Chance. [1]Frequentist probability or frequentism is an interpretation of probability; it defines an event's probability as the limit of its relative frequency in infinitely many trials (the long-run probability). [2]
Bayesian probability (/ ˈ b eɪ z i ə n / BAY-zee-ən or / ˈ b eɪ ʒ ən / BAY-zhən) [1] is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation [2] representing a state of knowledge [3] or as quantification of a personal belief.
Vol. 1: Probability and Probabilistic Causality. Vol. 2: Philosophy of Physics, Theory Structure and Measurement, and Action Theory. Jackson, Frank, and Robert Pargetter (1982) "Physical Probability as a Propensity," Noûs 16(4): 567–583. Khrennikov, Andrei (2009). Interpretations of probability (2nd ed.). Berlin New York: Walter de Gruyter.
Governments apply probabilistic methods in environmental regulation, entitlement analysis, and financial regulation. An example of the use of probability theory in equity trading is the effect of the perceived probability of any widespread Middle East conflict on oil prices, which have ripple effects in the economy as a whole.
From an epistemological perspective, the posterior probability contains everything there is to know about an uncertain proposition (such as a scientific hypothesis, or parameter values), given prior knowledge and a mathematical model describing the observations available at a particular time. [2]
Also confidence coefficient. A number indicating the probability that the confidence interval (range) captures the true population mean. For example, a confidence interval with a 95% confidence level has a 95% chance of capturing the population mean. Technically, this means that, if the experiment were repeated many times, 95% of the CIs computed at this level would contain the true population ...