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Posterior Probability =
P (E1|F) =
P(E1) P(F|E1) + P(E2) P(F|E2)
P (E1|F) =
Posterior probability (The revised values of prior
probabilities after receiving additional information)
P (E1) =
Prior probability (The probability which describes
decision maker's judgement about the states of the environment, future
events, or hypotheses, before obtaining additional information)
P (F|E1) =
Likelihood or "conditional probability" (the
probability of a
given sample result, observation or new item of information, under the
assumption that some particular hypothesis of state of the environment
Joint Probability (of prior and conditional probabilities)
-------------------------------------------------------- Marginal Probability (= Sum of all Joint Probabilities)
assigned to the joint
each survey result F (=new information) and each of the
Marginal (or unconditional) Probability
Probability that a
survey result occurs (are found by summing over the joint probabilities
for each of the survey results (F)
Curry, Leslie, Seasonal Programming and Bayesian Assessment of Atmospheric Resources, in: W.R.Derrick Sewell, ed., Human Dimensions of Weather Modification. Research Paper No. 105, Chicago: Department of Geography, University of Chicago, 1966, pp.127-38.
Earman, John. Bayes or Bust: A critical Examination of Bayesian Confirmation Theory. Cambridge: MIT Press, 1992. [Review in JEL Sept.1993, pp.1441-2.]
Gregori, Tullio, University of Trieste, Trieste, Italy A Bayesian approach to analyze regional elasticities [Abstract]
Grether, David M., "Testing Bayes Rule and the Representativeness Heuristic: Some Experimental Evidence," Journ.Econ.Behav.Organ. 17(1), Jan 1992, 31-57.
Hayter, Roger, Farmers' Crop Decisions and the Frost Hazard in East- Central Alberta: A Bayesian Approach, Tijdschrift voor Econ. en Sociale Geografie 66(2), 1975, 93-102.
Kahneman, Daniel, Paul Slovic and Amos Tversky, eds., Judgment under Uncertainty: Heuristics and Biases. Cambridge University Press, 1982.
King, L.J. and R.G.Golledge, "Bayesian Analysis and Models in Geographic Research," in Univ. of Iowa Geography Dept. Discussion Papers No.12, 1969, pp.15-45 [Geographical Essays Commemorating the Retirement of Harold H. McCarty]
Krumme, Gunter (1977)
Puri, Anil, Gökçe Soydemir:
Forecasting industrial employment figures in Southern California: A Bayesian vector autoregressive model
Ann Reg Sci 34 (2000) 4, 503-514
Article in PDF format (125 KB)
Withers, Suzanne D. "Quantitative Methods: Bayesian Inference, Bayesian
Thinking," Progress in Human Geography, 26(4), 2002, 553-66.
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