|
Archive@NYU >
Stern School of Business >
IOMS: Information Systems Working Papers >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14152
|
| Title: | Bayesian Analysis and Model Revision for kâth Order Markov
Chains with Unknown k. |
| Authors: | Radner, Roy |
| Keywords: | Bayesian analysis
model revision
bounded rationality |
| Issue Date: | 2-Oct-2002 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | CeDER-04-11 |
| Abstract: | mass 1 concentrated on the true process, provided that the prior
probability measure has full support and the true process is
irreducible. Second, I extend this result to the case in which k is
unbounded (but finite), which requires that the Bayesian decisionmaker
(DM) construct a prior on an infinite-dimensional parameter space.
Finally, in an alternative approach to this case, I suppose that the DM
considers a succession of models corresponding to larger and larger
values of k. Each time the DM revises his model he extends his prior
probability measure to the new - and larger - parameter space in a way
that is "consistent" with the previous prior, and recomputes
his posterior probability measures. I show that, roughly speaking, if
the DM does not revise his model âtoo frequently,â then
he will be increasingly confident that the current posterior is
increasingly concentrated on the true process. I motivate the procedure
of model revision by considerations of bounded rationality. |
| URI: | http://hdl.handle.net/2451/14152 |
| Appears in Collections: | CeDER Working Papers
IOMS: Information Systems Working Papers
|
All items in Faculty Digital Archive are protected by copyright, with all rights reserved.
|
