Name
Abstract | ||
|---|---|---|
In this paper, we formulate and solve a two-stage Bayesian sequential change diagnosis (SCD) problem in a multi-sensor setting. In the considered problem, the change propagates across the sensor array gradually. After a change is detected, we are allowed to continue observing more samples so that we can identify the distribution after the change more accurately. The goal is to minimize the total c... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/MLSP52302.2021.9596446 | 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP) |
Keywords | DocType | ISSN |
Costs,Conferences,Machine learning,Signal processing,Bayes methods,Delays,Computational complexity | Conference | 2161-0363 |
ISBN | Citations | PageRank |
978-1-7281-6338-3 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaochuan Ma | 1 | 0 | 1.01 |
| Lifeng Lai | 2 | 2289 | 167.78 |
| Shuguang Cui | 3 | 521 | 54.46 |