Name
Title | ||
|---|---|---|
Explicit estimation-error-probability computation and sensor design for flag Hidden Markov Models |
Abstract | ||
|---|---|---|
Hidden Markov Models (HMM) are used in a number of sensor networking applications. These applications often require performance evaluation and sensor design for HMM estimation algorithms. This article approaches the performance evaluation and design problems from a structural perspective. Specifically, for a special class of flag HMMs (where sensors accurately flag a subset of states), explicit formulae are derived for the average error probability of the maximum-likelihood estimate. These formulae are used to optimally place sensors, and to gain an understanding of the relationship between the HMMs structure and estimation error. Three examples, including a real-world case study on monitoring the elderly in a smart home, are presented. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/CISS.2015.7086876 | CISS |
Keywords | Field | DocType |
hidden markov models,maximum likelihood estimation,smart home,estimation,error probability,detectors,markov processes | Explicit formulae,Computer science,Home automation,Artificial intelligence,Computation,Hidden semi-Markov model,Mathematical optimization,Markov model,Algorithm,Variable-order Markov model,Probability of error,Hidden Markov model,Machine learning | Conference |
Citations | PageRank | References |
2 | 0.38 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kyle Doty | 1 | 2 | 1.06 |
| S. Roy | 2 | 42 | 5.91 |
| Dinuka Sahabandu | 3 | 2 | 2.07 |
| Ramyar Saeedi | 4 | 81 | 8.00 |