Name
Abstract | ||
|---|---|---|
Given two sequences of Gaussian data, the Behrens-Fisher problem is to infer whether there exists a difference between the two corresponding population means if the population variances are unknown. This paper examines the Behrens-Fisher-type problem within the minimum message length framework of inductive inference. Using a special bounding on a uniform prior over the population means, a simple Bayesian hypothesis test is derived that does not require computationally expensive numerical integration of the posterior distribution. The minimum message length procedure is then compared against well-known methods on the Behrens-Fisher hypothesis testing problem and the estimation of the common mean problem showing excellent performance in both cases. Extensions to the generalised Behrens-Fisher problem and the multivariate Behrens-Fisher problem are also discussed. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-44958-1_19 | ALGORITHMIC PROBABILITY AND FRIENDS: BAYESIAN PREDICTION AND ARTIFICIAL INTELLIGENCE |
Field | DocType | Volume |
Applied mathematics,Population,Minimum message length,Posterior probability,Gaussian,Fisher information,Behrens –Fisher problem,Statistical hypothesis testing,Mathematics,Bayesian probability | Conference | 7070 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.34 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enes Makalic | 1 | 55 | 11.54 |
| Daniel F. Schmidt | 2 | 51 | 10.68 |