A Recursive Algorithm for Computing Inferences in Imprecise Markov Chains
| Autor: | Natan T'Joens, Thomas Krak, Jasper De Bock, Gert de Cooman |
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| Rok vydání: | 2019 |
| Předmět: |
Class (set theory)
Markov chain Computational complexity theory Computer science Structure (category theory) Inference 02 engineering and technology Type (model theory) Exponential function 020204 information systems Product (mathematics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Algorithm |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030297640 ECSQARU Symbolic and Quantitative Approaches to Reasoning with Uncertainty-15th European Conference, ECSQARU 2019, Belgrade, Serbia, September 18-20, 2019, Proceedings Lecture Notes in Computer Science Lecture Notes in Computer Science-Symbolic and Quantitative Approaches to Reasoning with Uncertainty |
| ISSN: | 0302-9743 1611-3349 |
| Popis: | We present an algorithm that can efficiently compute a broad class of inferences for discrete-time imprecise Markov chains, a generalised type of Markov chains that allows one to take into account partially specified probabilities and other types of model uncertainty. The class of inferences that we consider contains, as special cases, tight lower and upper bounds on expected hitting times, on hitting probabilities and on expectations of functions that are a sum or product of simpler ones. Our algorithm exploits the specific structure that is inherent in all these inferences: they admit a general recursive decomposition. This allows us to achieve a computational complexity that scales linearly in the number of time points on which the inference depends, instead of the exponential scaling that is typical for a naive approach. |
| Databáze: | OpenAIRE |
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