Models for Uncertainty下载

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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

本期课程主要介绍华为的战略方法论BLM模型。 BLM是要素模型，最早是由IBM在集成产品开发IPD体系的基础上延伸而来的，可以有效指导企业甚至是个人做好前瞻性的规划。课程不仅仅是在讲方法论，同时也将方法论背后的底层逻辑呈现给了学员。尤其是在这样一个VUCA的时代，VUCA是指volatility（易变性）、uncertainty（不确定性）、complexity（复杂性）、ambiguity（模糊性）。无论是创业，还是作为职场人，对宏观的把控能力必将是你不可或缺的一种基础能力。在这样一个时代背景下，做好未来的战略布局尤为重要，因为只有看清了未来的发展趋势，你才能做到不焦虑、不迷茫。

结构不确定状态空间模型的鲁棒稳定性，肖扬，，我们提出一种2-D面检验确定正常稳定的线状态空间模型在实结构扰动下的稳定性。设系统矩阵是线性依赖于参数。由于不确定系统的矩阵

深度贝叶斯学习: In this work we develop tools to obtain practical uncertainty estimates in deep learning, casting recent deep learning tools as Bayesian models without changing either the models or the optimisation. In the first part of this thesis we develop the theory for such tools, providing applications and illustrative examples. We tie approximate inference in Bayesian models to dropout and other stochastic regularisation techniques, and assess the approximations empirically. We give example applications arising from this connection between modern deep learning and Bayesian modelling such as active learning of image data and data-efficient deep reinforcement learning. We further demonstrate the tools’ practicality through a survey of recent applications making use of the suggested techniques in language applications, medical diagnostics, bioinformatics, image processing, and autonomous driving. In the second part of the thesis we explore the insights stemming from the link between Bayesian modelling and deep learning, and its theoretical implications. We discuss what determines model uncertainty properties, analyse the approximate inference analytically in the linear case, and theoretically examine various priors such as spike and slab priors.

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