Name
Abstract | ||
|---|---|---|
We derive a new Bayesian Information Criterion (BIC) by formulating the problem of estimating the number of clusters in an observed dataset as maximization of the posterior probability of the candidate models. Given that some mild assumptions are satisfied, we provide a general BIC expression for a broad class of data distributions. This serves as a starting point when deriving the BIC for specifi... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TSP.2018.2866385 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Data models,Clustering algorithms,Signal processing algorithms,Partitioning algorithms,Bayes methods,Unsupervised learning,Analytical models | Data modeling,Mathematical optimization,Bayesian information criterion,Algorithm,Posterior probability,Unsupervised learning,Multivariate normal distribution,Cluster analysis,Mathematics,Maximization,Bayesian probability | Journal |
Volume | Issue | ISSN |
66 | 20 | 1053-587X |
Citations | PageRank | References |
1 | 0.37 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| freweyni k teklehaymanot | 1 | 8 | 3.51 |
| Michael Muma | 2 | 144 | 19.51 |
| Abdelhak M. Zoubir | 3 | 1036 | 148.03 |