这里是 ShowMeAI 持续分享的【开源eBook】系列！内容覆盖机器学习、深度学习、数据科学、数据分析、大数据、Keras、TensorFlow、PyTorch、强化学习、数学基础等各个方向。整理自各平台的原作者公开分享（审核大大请放手） ◉ 简介：本书对不确定条件下的决策算法作了广泛的介绍，内容涵盖了与决策有关的各种主题，介绍了基本的数学问题公式和解决这些问题的算法。 ◉ 目录： 第一部分：概率推理 - 表征 - 推理 - 参数学习 - 结构学习 - 简单决策 第二部分：顺序问题 - 精确解法 - 近似值函数 - 在线规划 - 政策搜索 - 政策梯度估计 - 政策梯度优化 - 角色批判方法 - 政策验证 第三部分：模型的不确定性 - 探索和利用 - 基于模型的方法 - 无模型的方法 - 模仿学习 第四部分：状态的不确定性 - 信念 - 准确的信念状态规划 - 离线信念状态规划 - 在线信念状态规划 - 控制器抽象 第五部分：多Agent系统 - 多Agent推理 - 序列问题 - 状态的不确定性 - 协作代理

hBayesDM hBayesDM （决策任务的多层贝叶斯建模）是一种用户友好的程序包，可对一系列决策任务上的各种计算模型提供分层的贝叶斯分析。 hBayesDM使用进行贝叶斯推理。 现在， hBayesDM支持和 ！ 快速链接 教程： : （R）和 （Python） 邮件列表： : forum / hbayesdm-users 错误报告： https : //github.com/CCS-Lab/hBayesDM/issues 贡献：请参阅此存储库的Wiki 。 引文 如果您使用hBayesDM或其某些代码进行研究，请引用本文： @article { hBayesDM , title = { Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making

蒙特卡罗树搜索方法 这是蒙特卡罗树搜索方法的Java实现。 它是独立的，与域无关的，因此可以轻松地在任何状态操作域中使用。 该项目是为我的学士学位论文目的而开发的。 依存关系 JUnit4，Java克隆库 用法 创建MctsDomainAgent的实现。 public class Player implements MctsDomainAgent< State> { ... } 创建MctsDomainState的实现。 public class State implements MctsDomainState< Action> { ... } 初始化搜索并调用uctSearchWithExploration（）以获得最有前途的操作。 Mcts< State> mcts = Mcts . initializeIterat

决策及相关信息decision making and relevant information

经典之作，不多用多介绍。欧美金融经济学或金融理论必推参考书。 John E. Ingersoll Rowman & Littlefield Publishers, Inc. Based on courses developed by the author over several years, this book provides access to a broad area of research that is not available in separate articles or books of readings. Topics covered include the meaning and measurement of risk, general single-period portfolio problems, mean-variance analysis and the Capital Asset Pricing Model, the Arbitrage Pricing Theory, complete markets, multiperiod portfolio problems and the Intertemporal Capital Asset Pricing Model, the Black-Scholes option pricing model and contingent claims analysis, "risk-neutral" pricing with Martingales, Modigliani-Miller and the capital structure of the firm, interest rates and the term structure, and others.

Decision Making Under Uncertainty Theory and Application. 2015 By Mykel J. Kochenderfer With Christopher Amato, Girish Chowdhary, Jonathan P. How, Hayley J. Davison Reynolds, Jason R. Thornton, Pedro A. Torres-Carrasquillo, N. Kemal Üre and John Vian Overview Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical enginee

Casuality, Correlation and Artificial Intelligence for Rational Decision Making By 作者: Tshilidzi Marwala ISBN-10 书号: 9814630861 ISBN-13 书号: 9789814630863 Release Finelybook 出版日期: March 5, 2015 pages 页数: (208)

斯坦福Algorithms for Decision Making，非图书无版权，管理员请仔细核对非图书无版权，管理员请仔细核对非图书无版权，管理员请仔细核对非图书无版权，管理员请仔细核对非图书无版权，管理员请仔细核对非图书无版权，管理员请仔细核对

Runtime Models Based on Dynamic Decision Networks: Enhancing the Decision-making in the Domain of Ambient Assisted Living Applications

斯坦福大学2021年1月，Mykel J. Kochenderfer教授主编。Board introduction to algorithms for optimal decision making under uncertainty. We cover a wide variety of topics related to decision making, introducing the underlying mathematical problem formulations and the algorithms for solving them.