Blackwell Prediction for Categorical Data
Hans Rudolf Lerche
TL;DR
This paper extends Blackwell's algorithm to sequentially predict categorical data, leveraging approachability theory to improve prediction strategies for 0-1 data.
Contribution
It generalizes Blackwell's approachability-based prediction algorithm to categorical data, broadening its applicability.
Findings
Successfully extended Blackwell's algorithm to categorical data
Demonstrated the effectiveness of the generalized approach
Built on Blackwell's approachability results from 1956
Abstract
We study the problem of sequential prediction of categorical data and discuss a generalisation of Blackwell's algorithm on 0-1 data. The arguments are based on Blackwell's approachability results given in Blackwell, D. (1956): An Analog of the Minimax Theorem for Vector Payoffs, Pacific Journal of Mathematics, 6, 1-8.
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Taxonomy
TopicsBayesian Modeling and Causal Inference · Advanced Statistical Methods and Models · Rough Sets and Fuzzy Logic