Principles and algorithms for causal reasoning with uncertainty:
Abstract: "This thesis examines representational issues that arise when reasoning about causes and effects given incomplete and uncertain knowledge about the domain. These issues are largely covered by the famous frame and qualification problems. This thesis shows how traditional nonmonotonic l...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Rochester, NY
1989
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| Schriftenreihe: | University of Rochester <Rochester, NY> / Department of Computer Science: Technical report
287 |
| Schlagworte: | |
| Zusammenfassung: | Abstract: "This thesis examines representational issues that arise when reasoning about causes and effects given incomplete and uncertain knowledge about the domain. These issues are largely covered by the famous frame and qualification problems. This thesis shows how traditional nonmonotonic logic approaches can be modified to address these problems in a simple, domain-dependent way. This nonmonotonic approach is then generalized to manipulate statistically-founded beliefs, allowing for more consistent and fine-grained representation for causal knowledge. In addition, this thesis presents an algorithmic approach for efficient parallel computation of statistical predictions. This approach involves two heuristics, highest impact first and highest impact remaining, which control the speed of convergence and error estimation for an algorithm that iteratively refines degrees of belief. This algorithm has been implemented and tested by a program called HITEST, which runs on parallel hardware." |
| Beschreibung: | Rochester, NY, Univ., Diss. |
| Beschreibung: | VII, 102 S. |
Internformat
MARC
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| 245 | 1 | 0 | |a Principles and algorithms for causal reasoning with uncertainty |c by Jay Charles Weber |
| 264 | 1 | |a Rochester, NY |c 1989 | |
| 300 | |a VII, 102 S. | ||
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| 490 | 1 | |a University of Rochester <Rochester, NY> / Department of Computer Science: Technical report |v 287 | |
| 500 | |a Rochester, NY, Univ., Diss. | ||
| 520 | 3 | |a Abstract: "This thesis examines representational issues that arise when reasoning about causes and effects given incomplete and uncertain knowledge about the domain. These issues are largely covered by the famous frame and qualification problems. This thesis shows how traditional nonmonotonic logic approaches can be modified to address these problems in a simple, domain-dependent way. This nonmonotonic approach is then generalized to manipulate statistically-founded beliefs, allowing for more consistent and fine-grained representation for causal knowledge. In addition, this thesis presents an algorithmic approach for efficient parallel computation of statistical predictions. This approach involves two heuristics, highest impact first and highest impact remaining, which control the speed of convergence and error estimation for an algorithm that iteratively refines degrees of belief. This algorithm has been implemented and tested by a program called HITEST, which runs on parallel hardware." | |
| 650 | 4 | |a Mathematical statistics | |
| 650 | 4 | |a Monotonic functions | |
| 655 | 7 | |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
| 810 | 2 | |a Department of Computer Science: Technical report |t University of Rochester <Rochester, NY> |v 287 |w (DE-604)BV008902697 |9 287 | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-005904681 | ||
Datensatz im Suchindex
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| author | Weber, Jay C. |
| author_facet | Weber, Jay C. |
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| author_sort | Weber, Jay C. |
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| callnumber-subject | QA - Mathematics |
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| indexdate | 2024-07-09T17:27:17Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-005904681 |
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| physical | VII, 102 S. |
| publishDate | 1989 |
| publishDateSearch | 1989 |
| publishDateSort | 1989 |
| record_format | marc |
| series2 | University of Rochester <Rochester, NY> / Department of Computer Science: Technical report |
| spelling | Weber, Jay C. Verfasser aut Principles and algorithms for causal reasoning with uncertainty by Jay Charles Weber Rochester, NY 1989 VII, 102 S. txt rdacontent n rdamedia nc rdacarrier University of Rochester <Rochester, NY> / Department of Computer Science: Technical report 287 Rochester, NY, Univ., Diss. Abstract: "This thesis examines representational issues that arise when reasoning about causes and effects given incomplete and uncertain knowledge about the domain. These issues are largely covered by the famous frame and qualification problems. This thesis shows how traditional nonmonotonic logic approaches can be modified to address these problems in a simple, domain-dependent way. This nonmonotonic approach is then generalized to manipulate statistically-founded beliefs, allowing for more consistent and fine-grained representation for causal knowledge. In addition, this thesis presents an algorithmic approach for efficient parallel computation of statistical predictions. This approach involves two heuristics, highest impact first and highest impact remaining, which control the speed of convergence and error estimation for an algorithm that iteratively refines degrees of belief. This algorithm has been implemented and tested by a program called HITEST, which runs on parallel hardware." Mathematical statistics Monotonic functions (DE-588)4113937-9 Hochschulschrift gnd-content Department of Computer Science: Technical report University of Rochester <Rochester, NY> 287 (DE-604)BV008902697 287 |
| spellingShingle | Weber, Jay C. Principles and algorithms for causal reasoning with uncertainty Mathematical statistics Monotonic functions |
| subject_GND | (DE-588)4113937-9 |
| title | Principles and algorithms for causal reasoning with uncertainty |
| title_auth | Principles and algorithms for causal reasoning with uncertainty |
| title_exact_search | Principles and algorithms for causal reasoning with uncertainty |
| title_full | Principles and algorithms for causal reasoning with uncertainty by Jay Charles Weber |
| title_fullStr | Principles and algorithms for causal reasoning with uncertainty by Jay Charles Weber |
| title_full_unstemmed | Principles and algorithms for causal reasoning with uncertainty by Jay Charles Weber |
| title_short | Principles and algorithms for causal reasoning with uncertainty |
| title_sort | principles and algorithms for causal reasoning with uncertainty |
| topic | Mathematical statistics Monotonic functions |
| topic_facet | Mathematical statistics Monotonic functions Hochschulschrift |
| volume_link | (DE-604)BV008902697 |
| work_keys_str_mv | AT weberjayc principlesandalgorithmsforcausalreasoningwithuncertainty |