Name
Abstract | ||
|---|---|---|
Hidden Markov Models (HMMs) have been widely utilized for modeling time series data in various engineering and biological systems. The analyses of these models are usually conducted using computer simulations and paper-and-pencil proof methods and, more recently, using probabilistic model-checking. However, all these methods either do not guarantee accurate analysis or are not scalable (for instance, they can hardly handle the computation when some parameters become very huge). As an alternative, we propose to use higher-order logic theorem proving to reason about properties of discrete HMMs by applying automated verification techniques. This paper presents some foundational formalizations in this regard, namely an extended-real numbers based formalization of finite-state Discrete-Time Markov chains and HMMs along with the verification of some of their fundamental properties. The distinguishing feature of our work is that it facilitates automatic verification of systems involving HMMs. For illustration purposes, we utilize our results for the formal analysis of a DNA sequence. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-11737-9_21 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
HMMs,HOL4,Theorem Proving,DNA,Probability Theory | Time series,Computer science,Automated theorem proving,Markov chain,Theoretical computer science,Artificial intelligence,Probabilistic logic,Probability theory,Hidden Markov model,Machine learning,Computation,Scalability | Conference |
Volume | ISSN | Citations |
8829 | 0302-9743 | 2 |
PageRank | References | Authors |
0.40 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| liya liu | 1 | 20 | 2.42 |
| Vincent Aravantinos | 2 | 86 | 10.29 |
| Osman Hasan | 3 | 401 | 60.79 |
| Sofiène Tahar | 4 | 915 | 110.41 |