摘要翻译：
我们描述了一种从随机环境中运动时跟踪的粒子进行统计学习的新方法。问题在于从记录的快照推断环境的属性。我们在这里考虑的情况下，一个流体种子相同的被动粒子扩散和平流的流动。我们的方法依赖于有效的算法来估计两个连续快照中粒子之间可能匹配的加权数，即底层图形模型的配分函数。然后，配分函数在模型参数，即扩散系数和速度梯度上最大化。一个信念传播(BP)方案是我们算法的支柱，为我们想要学习的流量参数提供准确的结果。此外，通过引入环路序列(LS)的贡献，对BP估计进行了改进。对于加权匹配问题，LS被简洁地表示为柯西积分，并通过鞍点逼近得到精确估计。数值实验表明，改进的BP算法与基于马尔可夫链蒙特卡罗(MCMC)方法的完全多项式随机逼近算法相比，其性能相当，而基于BP算法的算法速度明显快于MCMC算法。
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英文标题：
《Belief Propagation and Beyond for Particle Tracking》
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作者：
Michael Chertkov, Lukas Kroc, Massimo Vergassola
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最新提交年份：
2008
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Information Theory        信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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一级分类：Physics        物理学
二级分类：Statistical Mechanics        统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Mathematics        数学
二级分类：Information Theory        信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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一级分类：Physics        物理学
二级分类：Fluid Dynamics        流体动力学
分类描述：Turbulence, instabilities, incompressible/compressible flows, reacting flows. Aero/hydrodynamics, fluid-structure interactions, acoustics. Biological fluid dynamics, micro/nanofluidics, interfacial phenomena. Complex fluids, suspensions and granular flows, porous media flows. Geophysical flows, thermoconvective and stratified flows. Mathematical and computational methods for fluid dynamics, fluid flow models, experimental techniques.
湍流，不稳定性，不可压缩/可压缩流，反应流。气动/流体力学，流体-结构相互作用，声学。生物流体力学，微/纳米流体力学，界面现象。复杂流体，悬浮液和颗粒流，多孔介质流。地球物理流，热对流和层流。流体动力学的数学和计算方法，流体流动模型，实验技术。
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英文摘要：
  We describe a novel approach to statistical learning from particles tracked while moving in a random environment. The problem consists in inferring properties of the environment from recorded snapshots. We consider here the case of a fluid seeded with identical passive particles that diffuse and are advected by a flow. Our approach rests on efficient algorithms to estimate the weighted number of possible matchings among particles in two consecutive snapshots, the partition function of the underlying graphical model. The partition function is then maximized over the model parameters, namely diffusivity and velocity gradient. A Belief Propagation (BP) scheme is the backbone of our algorithm, providing accurate results for the flow parameters we want to learn. The BP estimate is additionally improved by incorporating Loop Series (LS) contributions. For the weighted matching problem, LS is compactly expressed as a Cauchy integral, accurately estimated by a saddle point approximation. Numerical experiments show that the quality of our improved BP algorithm is comparable to the one of a fully polynomial randomized approximation scheme, based on the Markov Chain Monte Carlo (MCMC) method, while the BP-based scheme is substantially faster than the MCMC scheme.
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PDF链接：
https://arxiv.org/pdf/0806.1199