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cs.IT2024
Asymptotically Minimax Regret by Bayes Mixtures
Jun'ichi Takeuchi, Andrew R. Barron
We study the problems of data compression, gambling and prediction of a sequence from an alphabet , in terms of regret and expected regret (redundancy)…
cs.IT2023
Improved MDL Estimators Using Fiber Bundle of Local Exponential Families for Non-exponential Families
Kohei Miyamoto, Andrew R. Barron, Jun'ichi Takeuchi
Minimum Description Length (MDL) estimators, using two-part codes for universal coding, are analyzed. For general parametric families under certain regularity conditions, we introd…