Evaluations for Estimation of an Information Source Based on State Decomposition
スポンサーリンク
概要
- 論文の詳細を見る
This paper's main objective is to analyze several procedures which select the model g among a set G of stochastic models to minimize the value of an information criterion in the form of L(g)=h[g](z^n)+k(g)/2c(n), where z^n is the n observed data emitted by an information source θ which consists of the model g_θ⋳G and k(g_θ) mutually independent stochastic parameters in the model g_θ⋳G,h[g](z^n) is (-1)×(the maximum log likelihood value of the data z^n with respect to a model g&sinsv;G), and c(n) is a predetermined function (penalty function) of n which controls the amount of penalty for increasing the model size. The result is focused on specific performances when the information criteria are applied to the framework of so-called state decomposition. Especially, upper bounds are derived of the following two performance measures for each penalty function c(n): the error probability of the model selection, and the average Kullback-Leibler information between the true information source and the estimated information source.
- 一般社団法人電子情報通信学会の論文
- 1993-07-25