马尔科夫链是概率统计中的一个重要模型,也是机器学习和人工智能的基石,在强化学习、自然语言处理、金融领域、天气预测等方面都有着极其广泛的应用.其数学定义为:假设我们的序列状态是…,
![](https://math.21cnjy.com/MathMLToImage?mml=%3Cmath+xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F1998%2FMath%2FMathML%22%3E%3Cmsub%3E%3Cmrow%3E%3Cmi%3EX%3C%2Fmi%3E%3C%2Fmrow%3E%3Cmrow%3E%3Cmi%3Et%3C%2Fmi%3E%3Cmo%3E%E2%88%92%3C%2Fmo%3E%3Cmn%3E2%3C%2Fmn%3E%3C%2Fmrow%3E%3C%2Fmsub%3E%3C%2Fmath%3E)
,
![](https://math.21cnjy.com/MathMLToImage?mml=%3Cmath+xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F1998%2FMath%2FMathML%22%3E%3Cmsub%3E%3Cmrow%3E%3Cmi%3EX%3C%2Fmi%3E%3C%2Fmrow%3E%3Cmrow%3E%3Cmi%3Et%3C%2Fmi%3E%3Cmo%3E%E2%88%92%3C%2Fmo%3E%3Cmn%3E1%3C%2Fmn%3E%3C%2Fmrow%3E%3C%2Fmsub%3E%3C%2Fmath%3E)
,
![](https://math.21cnjy.com/MathMLToImage?mml=%3Cmath+xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F1998%2FMath%2FMathML%22%3E%3Cmsub%3E%3Cmrow%3E%3Cmi%3EX%3C%2Fmi%3E%3C%2Fmrow%3E%3Cmrow%3E%3Cmi%3Et%3C%2Fmi%3E%3C%2Fmrow%3E%3C%2Fmsub%3E%3C%2Fmath%3E)
,
![](https://math.21cnjy.com/MathMLToImage?mml=%3Cmath+xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F1998%2FMath%2FMathML%22%3E%3Cmsub%3E%3Cmrow%3E%3Cmi%3EX%3C%2Fmi%3E%3C%2Fmrow%3E%3Cmrow%3E%3Cmi%3Et%3C%2Fmi%3E%3Cmo%3E%2B%3C%2Fmo%3E%3Cmn%3E1%3C%2Fmn%3E%3C%2Fmrow%3E%3C%2Fmsub%3E%3C%2Fmath%3E)
, …,那么
![](https://math.21cnjy.com/MathMLToImage?mml=%3Cmath+xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F1998%2FMath%2FMathML%22%3E%3Cmsub%3E%3Cmrow%3E%3Cmi%3EX%3C%2Fmi%3E%3C%2Fmrow%3E%3Cmrow%3E%3Cmi%3Et%3C%2Fmi%3E%3Cmo%3E%2B%3C%2Fmo%3E%3Cmn%3E1%3C%2Fmn%3E%3C%2Fmrow%3E%3C%2Fmsub%3E%3C%2Fmath%3E)
时刻的状态的条件概率仅依赖前一状态
![](https://math.21cnjy.com/MathMLToImage?mml=%3Cmath+xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F1998%2FMath%2FMathML%22%3E%3Cmsub%3E%3Cmrow%3E%3Cmi%3EX%3C%2Fmi%3E%3C%2Fmrow%3E%3Cmrow%3E%3Cmi%3Et%3C%2Fmi%3E%3C%2Fmrow%3E%3C%2Fmsub%3E%3C%2Fmath%3E)
, 即
![](https://math.21cnjy.com/MathMLToImage?mml=%3Cmath+xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F1998%2FMath%2FMathML%22%3E%3Cmi%3EP%3C%2Fmi%3E%3Cmrow%3E%3Cmo%3E%28%3C%2Fmo%3E%3Cmrow%3E%3Cmrow%3E%3Cmrow%3E%3Cmsub%3E%3Cmrow%3E%3Cmi%3EX%3C%2Fmi%3E%3C%2Fmrow%3E%3Cmrow%3E%3Cmi%3Et%3C%2Fmi%3E%3Cmo%3E%2B%3C%2Fmo%3E%3Cmn%3E1%3C%2Fmn%3E%3C%2Fmrow%3E%3C%2Fmsub%3E%3C%2Fmrow%3E%3Cmo%3E%7C%3C%2Fmo%3E%3C%2Fmrow%3E%3Cmo%3E%E2%8B%85%3C%2Fmo%3E%3Cmo%3E%E2%8B%85%3C%2Fmo%3E%3Cmo%3E%E2%8B%85%3C%2Fmo%3E%3Cmn%3E++%EF%BC%8C+%3C%2Fmn%3E%3Cmsub%3E%3Cmrow%3E%3Cmi%3EX%3C%2Fmi%3E%3C%2Fmrow%3E%3Cmrow%3E%3Cmi%3Et%3C%2Fmi%3E%3Cmo%3E%E2%88%92%3C%2Fmo%3E%3Cmn%3E2%3C%2Fmn%3E%3C%2Fmrow%3E%3C%2Fmsub%3E%3Cmn%3E++%EF%BC%8C+%3C%2Fmn%3E%3Cmsub%3E%3Cmrow%3E%3Cmi%3EX%3C%2Fmi%3E%3C%2Fmrow%3E%3Cmrow%3E%3Cmi%3Et%3C%2Fmi%3E%3Cmo%3E%E2%88%92%3C%2Fmo%3E%3Cmn%3E1%3C%2Fmn%3E%3C%2Fmrow%3E%3C%2Fmsub%3E%3Cmn%3E++%EF%BC%8C+%3C%2Fmn%3E%3Cmsub%3E%3Cmrow%3E%3Cmi%3EX%3C%2Fmi%3E%3C%2Fmrow%3E%3Cmrow%3E%3Cmi%3Et%3C%2Fmi%3E%3C%2Fmrow%3E%3C%2Fmsub%3E%3C%2Fmrow%3E%3Cmo%3E%29%3C%2Fmo%3E%3C%2Fmrow%3E%3Cmo%3E%3D%3C%2Fmo%3E%3Cmi%3EP%3C%2Fmi%3E%3Cmrow%3E%3Cmo%3E%28%3C%2Fmo%3E%3Cmrow%3E%3Cmrow%3E%3Cmrow%3E%3Cmsub%3E%3Cmrow%3E%3Cmi%3EX%3C%2Fmi%3E%3C%2Fmrow%3E%3Cmrow%3E%3Cmi%3Et%3C%2Fmi%3E%3Cmo%3E%2B%3C%2Fmo%3E%3Cmn%3E1%3C%2Fmn%3E%3C%2Fmrow%3E%3C%2Fmsub%3E%3C%2Fmrow%3E%3Cmo%3E%7C%3C%2Fmo%3E%3C%2Fmrow%3E%3Cmsub%3E%3Cmrow%3E%3Cmi%3EX%3C%2Fmi%3E%3C%2Fmrow%3E%3Cmrow%3E%3Cmi%3Et%3C%2Fmi%3E%3C%2Fmrow%3E%3C%2Fmsub%3E%3C%2Fmrow%3E%3Cmo%3E%29%3C%2Fmo%3E%3C%2Fmrow%3E%3C%2Fmath%3E)
.
现实生活中也存在着许多马尔科夫链,例如著名的赌徒模型.
假如一名赌徒进入赌场参与一个赌博游戏,每一局赌徒赌赢的概率为
, 且每局赌赢可以赢得1元,每一局赌徒赌输的概率为
, 且赌输就要输掉1元.赌徒会一直玩下去,直到遇到如下两种情况才会结束赌博游戏:一种是手中赌金为0元,即赌徒输光;一种是赌金达到预期的B元,赌徒停止赌博.记赌徒的本金为
, 赌博过程如下图的数轴所示.
![](http://tikupic.21cnjy.com/2023/04/14/43/db/43db1a781608eb7e001671f755a70a7b_526x160.png)
当赌徒手中有n元(
,
)时,最终输光的概率为
, 请回答下列问题: