Knowledge and reasoning with uncertainty modeling: matrix-and-vector calculus for local reconciliation of truth estimates
Keywords:
knowledge with uncertainty, reasoning with uncertainty, reasoning modeling, algebraic Bayesian network, conjunct ideal, probabilistic logicAbstract
In the theory of algebraic Bayesian networks, there are four operations classified as a kind of local synthesis of consistent truth estimates: knowledge pattern consistency verification, knowledge pattern reconciliation, a posteriori inference, and knowledge pattern enclosing reconciliation. The paper presents a knowledge pattern model formalization based on the matrix-vector terms. The model itself is a conjuncts ideal with scalar or interval truth probabilistic estimates. A specification of all four operations of the local synthesis has been introduced in the matrix-vector terms.References
Published
2011-09-01
How to Cite
Sirotkin, A., & Tulupyev, A. (2011). Knowledge and reasoning with uncertainty modeling: matrix-and-vector calculus for local reconciliation of truth estimates. SPIIRAS Proceedings, 3(18), 108-135. https://doi.org/10.15622/sp.18.5
Section
Articles
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
