LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Faria, Alvaro Eduardo
Languages: English
Types: Doctoral thesis
Subjects: QA
This thesis addresses the multivariate version of the group decision problem (French,\ud 1985), where the opinions about the possible values of n random variables in a problem,\ud expressed as subjective conditional probability density functions of the k members of a\ud group of experts, are to be combined together into a single probability density.\ud A particular type of graphical chain model a bit more general than an influence diagram\ud defined as a partially complete chain graph (PCG) is used to describe the multivariate\ud causal (ordered) structure of associations between those n random variables. It is assumed\ud that the group has a commonly agreed. PCG but the members diverge about the actual\ud conditional probability densities for the component variables in the common PCG. From\ud this particular situation we investigate some suitable solutions.\ud The axiomatic approach to the group decision problem suggests that the group adopts\ud a combination algorithm which demands, at least on learning information which is common\ud to the members and which preserves the originally agreed PCG structure, that the\ud pools of conditional densities associated with the PCG are externally Bayesian (Madansky,\ud 1964). We propose a logarithmic characterisation for such conditionally externally\ud Bayesian (CEB) poolings which is more flexible than the logarithmic characterisation\ud proposed by Genest et al. (1986). It is illustrated why such a generalisation is practically\ud quite useful allowing, for example, the weights attributed to the joint probability assessments\ud of different individuals in the pool to differ across the distinct conditional probability\ud densities which compound each joint density. A major advantage of this scheme is that\ud it may allow the weights given to the group's members to vary according to the areas of\ud prediction they can perform best. It is also shown that the group's commitment to being\ud CEB on chain elements can be accomplished with the group appearing externally Bayesian\ud on the whole PCG. Another feature of the CEB logarithmic pools is that with them the\ud impossibility theorems related to the preservation of independence by opinion pools can\ud be avoided. Yet, in the context of the axiomatic approach, we show the conditions under\ud which the types of pools that satisfy McConway's (1981) marginalization property, i.e. the\ud linear pools, can also be CEB.\ud Also, the expert judgement problem (French, 1985) is investigated through the Bayesian\ud modelling approach where a supra-Bayesian decision maker treats the experts' opinions\ud as data in the usual Bayesian framework. Graphical representations of standard combination\ud models are discussed in the light of the issues of dependence among experts and\ud sufficiency of experts' statements in certain cases. Most importantly, a supra-Bayesian\ud analysis of uncalibrated experts allows the establishment of a link between the axiomatic\ud and Bayesian modelling approaches. Reconciliation rules which are externally Bayesian\ud are obtained. This result most naturally extends those rules to be CEB in the above\ud mentioned multivariate structures.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1.2 Historical 2.1 Some Fundamental 8.4 The Binomial
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article