Summary
The relation of statistical inference to the wider problem of all inductive inference is reviewed. For scientific inference in general the competing approaches are the hypothetical-deductive and the Bayesian, and the formalism of each is discussed in statistical contexts in terms of the two main concepts of probability—chance and degree of belief.
Inference problems arising with stochastic processes and time-series are considered against this background, and the author’s own general attitude to statistical inference reiterated.
Two appendices refer respectively to two specific technical problems (i) separating a discrete and a spectral density component, (ii) specification and inference for "nearest-neighbour" systems.
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Bartlett, M.S. (1975). Inference and stochastic processes. In: Probability, Statistics and Time. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-5889-0_4
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DOI: https://doi.org/10.1007/978-94-009-5889-0_4
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