Fundamentals of statistical exponential families: with applications in statistical decision theory
Fundamentals of statistical exponential families: with applications in statistical decision theory
Fisher information and stochastic complexity
IEEE Transactions on Information Theory
Markov types and minimax redundancy for Markov sources
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
We study Fisher information of stationary Markov models with a finite alphabet. In particular, we derive the Fisher information determinant of expectation parameter η, which is defined as expectation of Markov type. The Fisher information determinant with respect to Markov kernel parameter (conditional probabilities) is easy to find, while it is not so with respect to the expectation parameter η nor the natural parameter θ. Note that θ and η are of special importance for exponential families including Markov models.