Name
Abstract | ||
|---|---|---|
Imagine a random walk that outputs a state only when visiting it for the first time. The observed output is therefore a repeat-censored version of the underlying walk, and consists of a permutation of the states or a prefix of it. We call this model initial-visit emitting random walk (INVITE). Prior work has shown that the random walks with such a repeat-censoring mechanism explain well human behavior in memory search tasks, which is of great interest in both the study of human cognition and various clinical applications. However, parameter estimation in INVITE is challenging, because naive likelihood computation by marginalizing over infinitely many hidden random walk trajectories is intractable. In this paper, we propose the first efficient maximum likelihood estimate (MLE) for INVITE by decomposing the censored output into a series of absorbing random walks. We also prove theoretical properties of the MLE including identifiability and consistency. We show that INVITE outperforms several existing methods on real-world human response data from memory search tasks. |
Year | Venue | Field |
|---|---|---|
2015 | Annual Conference on Neural Information Processing Systems | Loop-erased random walk,Random walk,Computer science,Identifiability,Permutation,Prefix,Theoretical computer science,Artificial intelligence,Estimation theory,Cognition,Machine learning,Computation |
DocType | Volume | ISSN |
Conference | 28 | 1049-5258 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kwang-Sung Jun | 1 | 63 | 9.11 |
| Xiaojin Zhu | 2 | 3586 | 222.74 |
| Timothy T. Rogers | 3 | 102 | 21.17 |
| zhuoran yang | 4 | 52 | 29.86 |
| Ming Yuan | 5 | 195 | 22.42 |