Probability and Computing: Randomized Algorithms and Probabilistic AnalysisChapter 7 begins with a nice introduction of the importance of asymptotic distribution theory for all statisticians, theoretical and applied. There is excellent motivation and explanation of convergence in distribution (law), ...
A probabilistic analysis of a Beverton-Holt type discrete model: Theoretical and computing analysisfirst and second probability density functionsrandom variable transformation methodrandomized Beverton-Holt–type discrete modelIn this paper a randomized version of the Beverton-Holt type discrete model is ...
GenCast is trained on 40 years of best-estimate analysis from 1979 to 2018, taken from the publicly available ERA5 (fifth generation ECMWF reanalysis) reanalysis dataset27. Reanalysis provides a reconstruction of past weather by computing analysis for historical dates and times. For simplicity, w...
Data Mining Association Analysis Basic Concepts and Alg:数据挖掘中的关联分析的基本概念和算法 热度: 教育数据挖掘手册Handbook of Educational Data Mining 2010 热度: Mining Uncertain and Probabilistic Data Problems, Challenges, Methods, and Applications ...
The variance instead scales divisively with the square of λ, which in turn determines the reduction of variability (the FF in Eq. 3) by surround stimulation. This analysis thus shows that, in the GSM inference, divisive normalization influences both the mean and the variance of the posterior...
TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and scalability to large da...
… MCMAS is a tool for model checking of strategic epistemic logic with multi-agent systems … the aims of the system with user stories in controlled natural language, augmented by … used quantitative analysis methods such as stochastic simulation and probabilistic model checking …...
Victor Lee, Kyle Cranmer, Prabhat, and Frank Wood. 2019. “Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale.” In Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC19), November 17–22, 2019.arXiv:1907.03382...
The combination of artificial intelligence with data, computing power, and new algorithms can provide important tools for solving engineering problems, such as machine-tool condition monitoring. However, many of these problems require algorithms that can perform in highly dynamic scenarios where the data...
Execution of probabilistic computing algorithms require electrically programmable stochasticity to encode arbitrary probability functions and controlled stochastic interaction or correlation between probabilistic (p-) bits. The latter is implemented with complex electronic components leaving a large footprint on a...