1 INTRODUCTION 1 1.1 Uncertainty in Robotics 1 1.2 Probabilistic Robotics 3 1.3 Implications 5 1.4 Road Map 6 1.5 Bibliographical Remarks 7 2 RECURSIVE STATE ESTIMATION 9 2.1 Introduction 9 2.2 Basic Concepts in Probability 10 2.3 Robot Environment Interaction 16 2.3.1 State 16 2.3.2 Environment Interaction 18 2.3.3 Probabilistic Generative Laws 20 2.3.4 Belief Distributions 22 2.4 Bayes Filters 23 2.4.1 The Bayes Filter Algorithm 23 2.4.2 Example 24 2.4.3 Mathematical Derivation of the Bayes Filter 28 2.4.4 The Markov Assumption 30 2.5 Representation and Computation 30 2.6 Summary 31 2.7 Bibliographical Remarks 32

概率图模型，全书1000多页，高清pdf版，注意，不是扫描版。 此书是一切有志研究机器学习或数据挖掘的同学必看的一本书，涵盖了所有概率的图模型，包括马尔科夫逻辑网，贝叶斯网络，等等很多很多。非常珍贵的资源

剩余使用寿命（RUL）预测在预测和健康管理（PHM）中起着至关重要的作用，以提高可靠性并降低众多机械系统的周期成本。 深度学习（DL）模型，尤其是深度卷积神经网络（DCNN），在RUL预测中正变得越来越流行，从而在最近的研究中取得了最新的成果。 大多数DL模型仅提供目标RUL的点估计，但是非常需要为任何RUL估计具有关联的置信区间。 为了改进现有方法，我们构建了一个概率RUL预测框架，以基于参数和非参数方法来估计目标输出的概率密度。 模型输出是对目标RUL的概率密度的估计，而不仅仅是单点估计。 所提出的方法的主要优点是该方法自然可以提供目标预测的置信区间（不确定性）。 我们通过一个简单的DCNN模型，在公开可用的涡轮发动机退化模拟数据集上验证了我们构建的框架的有效性。 源代码将在https://github.com/ZhaoZhibin/Probabilistic_RUL_Prediction中发布。

三本机器人领域的著作，1 BRUNO Siciliano, Robotics Modeling,Planning and Control. 2 Sebastian Thrun, Wolfram Burgard, Dieter Fox, Probabilistic Robotics, The MIT Press, 2005. 3 Probabilistic Robotics中文对照版本

概率图模型是机器学习中的一种技术，它利用图论的概念来简明地表示和优化预测数据问题中的值。 图形模型为我们提供了在数据中寻找复杂模式的技术，并广泛应用于语音识别、信息提取、图像分割和基因调控网络建模等领域。 本书从概率论和图论的基础出发，接着讨论各种模型和推理算法。讨论了所有不同类型的模型以及创建和修改模型的代码示例，并对它们运行了不同的推理算法。有一整章将继续介绍朴素的贝叶斯模型和隐藏的马尔可夫模型。这些模型已经用实际例子进行了深入的讨论。

本书旨在用作研究生水平的教科书和技术参考。 重点是基本概念，分析技术和基本经验方法。 唯一的前提条件是有关傅立叶分析的入门课程。

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students. 优点：新，全！ 由于成书时间较晚，所以涵盖了更多最近几年的hot topic，比如Dirichlet Process 。 更重要的，是全，基本上ML领域的专有名词，你都可以在书后的index找到。说道这里，不得不佩服本书的作者Kevin Murphy，剑桥的本科，UCB的博士，MIT的博后，得到过多位大牛的真传 。 还有一个非常重要的，就是这本书配备了详尽的matlab code，你几乎可以尝试书中的每一个例子。 单从以上这几点，绝对应该把他排在所有ML教材的首位！

With the ever increasing amounts of data in electronic form, the need for automated methods for data analysis continues to grow. The goal of machine learning is to develop methods that can automatically detect patterns in data, and then to use the uncovered patterns to predict future data or other outcomes of interest. Machine learning is thus closely related to the fields of statistics and data mining, but differs slightly in terms of its emphasis and terminology. This book provides a detailed introduction to the field, and includes worked examples drawn from application domains such as molecular biology, text processing, computer vision, and robotics.

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概率预测工具 根据团队交付时间分布预测一堆故事需要多长时间 为 Sky Network Services Hack 日创建 积压与进展 您可以在我们的跟踪我们的努力和未来计划 资源 特洛伊·马格尼斯 迪米塔尔·巴卡尔吉耶夫#NoEstimates 使用蒙特卡罗模拟项目规划