“…To accurately and reliably estimate the motion state of each LiDAR point considering the measurement uncertainty, both probability theory and evidence theory are employed in the segmentation algorithm. …”
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“…The following methods were used: systematic, functional and comparative analysis, risk theory, mathematical modeling, probability theory and mathematical statistics, as well as geographic information system technologies for digital map design and objective-oriented methodology for software design systems. …”
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“…An analogy to the classic “coupon collector” problem of probability theory reveals that the takeover time [Formula: see text] is dominated by extremal behavior, either when there are only a few infected nodes near the start of the process or a few susceptible nodes near the end. …”
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“…Therefore, the methods, such as probability theory and information theory play a significant role in estimating the uncertainty. …”
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“…We study model embeddability, which is a variation of the famous embedding problem in probability theory, when apart from the requirement that the Markov matrix is the matrix exponential of a rate matrix, we additionally ask that the rate matrix follows the model structure. …”
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“…Quantum information theorists have created axiomatic reconstructions of quantum mechanics (QM) that are very successful at identifying precisely what distinguishes quantum probability theory from classical and more general probability theories in terms of information-theoretic principles. …”
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“…The historical laboratory data were assumed to be error-free. Using the probability theory, the clinical specificity of a set of autoverification limits can be derived by calculating the percentile values of the overall distribution of a measurand. …”
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“…Features: ·         self-contained treatment explaining Monte Carlo and Las Vegas randomized algorithms from their genesis in the principles of probability theory to their use for system analysis; ·         development of a novel paradigm for (convex and nonconvex) controller synthesis in the presence of uncertainty and in the context of randomized algorithms; ·         comprehensive treatment of multivariate sample generation techniques, including consideration of the difficulties involved in obtaining identically and independently distributed samples; ·         applications of randomized algorithms in various endeavours, such as PageRank computation for the Google Web search engine,  unmanned aerial vehicle design (both new in the second edition), congestion control of high-speed communications networks and stability of quantized sampled-data systems.   …”
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“…The revised edition features  New coverage of the rendering equation, GPU architecture considerations, and importance-sampling in physically based rendering An emphasis on modern approaches, as in a new chapter on probability theory for use in Monte-Carlo rendering Concrete principles of graphics algorithm design Implementations of hardware shaders, software rendering, and graphics-intensive 3D interfaces 3D real-time graphics platforms—their design goals and trade-offs—including new mobile and browser platforms Programming and debugging approaches unique to graphics development  The text and hundreds of figures are presented in full color throughout the book. …”
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“…Building upon the previous editions, this textbook is a first course in stochastic processes taken by undergraduate and graduate students (MS and PhD students from math, statistics, economics, computer science, engineering, and finance departments) who have had a course in probability theory. It covers Markov chains in discrete and continuous time, Poisson processes, renewal processes, martingales, and option pricing. …”
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“…As the name Central Limit Theorem suggests, it is a centerpiece of probability theory which also carries over to statistics. …”
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“…It starts with a quick introduction to probability theory and stochastic processes and continues with chapters on Markov chains and regenerative processes. …”
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“…The final yields match the theoretical yields from probability theory for simple motifs and overestimate experimental yields, which constitute lower bounds, for aptamers because screening analyses beyond secondary structure information are not considered systematically. …”
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“…Information theory, a branch of probability theory dealing explicitly with the quantification of uncertainty, has been proposed as an alternative framework for diagnostic test interpretation, but is even less familiar to physicians. …”
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“…The framework we develop relies on a blend of ideas from probability theory, linear algebra and optimization theory. …”
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“…This mathematical formalization is derived from probability theory and synergetics. We draw the conclusion that structural theory provides powerful tools for the modeling of how therapeutic change is staged by the formation, utilization, and dissolution of the therapeutic alliance. …”
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“…This insight allowed us to apply the standard laws of probability theory to combine restraints from multiple templates. …”
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“…Surprise, a resolution-limit-free function rooted in discrete probability theory, has been recently introduced and applied to brain networks, revealing a wide size-distribution of functional modules (Nicolini and Bifone, 2016), in contrast with many previous reports. …”
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“…To enable reactive chemical systems to compute we show how, using the Cox interpretation of probability theory, one can transcribe the equations of chemical kinetics as a sequence of coupled logic gates operating on continuous variables. …”
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“…However, the exact number of required replicates enabling the detection of such rare events often remains a mystery but can be approached through probability theory. Here, we describe a probabilistic model, relating the number of observed events in a batch of samples with observation probabilities. …”
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