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题名:	An adaptive Kelly betting strategy for finite repeated games
作者:	左瑞麟 Wu, Mu-En Tsai, Hui-Huang Tso, Raylin Weng, Chi-Yao
贡献者:	資科系
关键词:	Computation theory;Profitability;Kelly criterion;KL-divergence;Learning Theory;Odds;Winning probability;Probability
日期:	2015-08
上传时间:	2017-08-08 17:00:22 (UTC+8)
摘要:	Kelly criterion is the optimal bidding strategy when considering a series of gambles with the wining probability p and the odds b. One of the arguments is Kelly criterion is optimal in theory rather than in practice. In this paper we show the results of using Kelly criterion in a gamble of bidding T steps. At the end of T steps, there are W times of winning and L times of losing. i.e. T =W + L. Consequently, the best strategy for these bidding steps is using the probability W/T instead of using p in Kelly Criterion. However, we do not know the number of W, to put it better the information of p, before placing the bet. We first derive the relation of profits between using p and W/T as the winning probability in the Kelly formula, respectively. Then we use the proportion of winning and bidding numbers before time step t, denoted as t p, as the winning probability used in the Kelly criterion at time step t. Even we do not know the winning probability of p in a gamble, we can use this method to achieve the profit near the optimal profit when using p in the Kelly betting. © Springer International Publishing Switzerland 2016.
關聯:	Advances in Intelligent Systems and Computing, 388, 39-46 9th International Conference on Genetic and Evolutionary Computing, ICGEC 2015; Yangon; Myanmar; 26 August 2015 到 28 August 2015; 代碼 141219
数据类型:	conference
DOI 連結:	http://dx.doi.org/10.1007/978-3-319-23207-2_5
DOI:	10.1007/978-3-319-23207-2_5
显示于类别:	[資訊科學系] 會議論文

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