000			01599nam a2200181 4500
005			20250122153507.0
008			250122b |||||||| |||| 00| 0 eng d
020			_a9781638280828
082			_a519.542 _bPOW
100			_aPowell, Warren B _917997
245			_aSequential decision analytics and modeling: _bmodeling with python
260			_bNow publishers Inc _aBoston _c2022
300			_a307 p.
365			_aUSD _b99.00
520			_aSequential decision problems arise in virtually every human process, spanning finance, energy, transportation, health, e-commerce and supply chains. They include pure learning problems as might arise in laboratory (or field) experiments. It even covers search algorithms to maximize uncertain functions. An important dimension of every problem setting is the need to make decisions in the presence of different forms of uncertainty, and evolving information processes. This book uses a teach-by-example style to illustrate a modeling framework that can represent any sequential decision problem. A major challenge is, then, designing methods (called policies) for making decisions. We describe four classes of policies that are universal, in that they span any method that might be used, whether from the academic literature or heuristics used in practice. While this does not mean that we can immediately solve any problem, the framework helps us avoid the tendency in the academic literature of focusing on narrow classes of methods. (https://www.nowpublishers.com/article/Details/TOM-103-II)
650			_aAnalytics and modeling _920872
942			_cBK _2ddc
999			_c7442 _d7442