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Selected Geographic Resources in
Bayesian Analysis
Supporting & Related Pages:
Basic Introduction:
Posterior Probability =  P (E1F)  = 
P(E1) P(FE1)
 P(E1) P(FE1) + P(E2) P(FE2) 
P (E1F) =  Posterior probability (The revised values of prior probabilities after receiving additional information) 
P (E1) =  Prior probability (The probability which describes the decision maker's judgement about the states of the environment, future events, or hypotheses, before obtaining additional information) 
P (FE1) =  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 prevails) 
Posterior Probability  = 
Joint Probability (of prior and conditional probabilities)

Joint Probability  =  Probability assigned to the joint occurence of each survey result F (=new information) and each of the underlying/possible events 
Marginal (or unconditional) Probability  =  Probability that a particular survey result occurs (are found by summing over the joint probabilities for each of the survey results (F) 
Internet Sites
Clippings:
Literature:
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.12738.
Earman, John. Bayes or Bust: A critical Examination of Bayesian Confirmation Theory. Cambridge: MIT Press, 1992. [Review in JEL Sept.1993, pp.14412.]
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, 3157.
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, 93102.
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.1545 [Geographical Essays Commemorating the Retirement of Harold H. McCarty]
Puri, Anil, Gökçe Soydemir:
Forecasting
industrial employment figures in Southern California: A Bayesian vector
autoregressive model
Ann Reg Sci 34 (2000) 4, 503514
Article
in PDF format (125 KB)
Withers, Suzanne D. "Quantitative Methods: Bayesian Inference, Bayesian
Thinking," Progress in Human Geography, 26(4), 2002, 55366.
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