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F. Perron and E. Marchand (2002), "On the minimax estimator of a bounded normal mean," Statistics and Probability Letters 58: 327–333. R. Fandom Noubiap and W. Seidel (2001), "An Algorithm for Calculating Gamma-Minimax Decision Rules under Generalized Moment Conditions," Annals of Statistics, August, 2001, vol. 29, no. 4, pp. 1094–1116
Truncated Chebyshev series, however, closely approximate the minimax polynomial. One popular minimax approximation algorithm is the Remez algorithm. References
It is always true that the left-hand side is at most the right-hand side (max–min inequality) but equality only holds under certain conditions identified by minimax theorems. The first theorem in this sense is von Neumann's minimax theorem about two-player zero-sum games published in 1928, [2] which is considered the starting point of game ...
In computational complexity theory, Yao's principle (also called Yao's minimax principle or Yao's lemma) relates the performance of randomized algorithms to deterministic (non-random) algorithms. It states that, for certain classes of algorithms, and certain measures of the performance of the algorithms, the following two quantities are equal:
Leonard J. Savage argued that using non-Bayesian methods such as minimax, the loss function should be based on the idea of regret, i.e., the loss associated with a decision should be the difference between the consequences of the best decision that could have been made under circumstances will be known and the decision that was in fact taken before they were known.
These rules are often inadmissible and the verification of their admissibility can be difficult. For example, the generalized Bayes estimator of a location parameter θ based on Gaussian samples (described in the "Generalized Bayes estimator" section above) is inadmissible for p > 2 {\displaystyle p>2} ; this is known as Stein's phenomenon .
In the statistical decision theory, where one is faced with making decisions in the presence of statistical knowledge, Γ-minimax inference is a minimax approach used to deal with partial prior information. It works with applications of Γ-minimax to statistical estimation, and contains Γ-minimax theory, used to pick applicable decision rules ...
Alpha–beta pruning is a search algorithm that seeks to decrease the number of nodes that are evaluated by the minimax algorithm in its search tree. It is an adversarial search algorithm used commonly for machine playing of two-player combinatorial games (Tic-tac-toe, Chess, Connect 4, etc.). It stops evaluating a move when at least one ...