field particle वाक्य
उदाहरण वाक्य
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- New classes of mean field particle simulation techniques for Feynman-Kac path-integration problems includes genealogical tree based models, backward particle models, adaptive mean field particle models, island type particle models, and particle Markov chain Monte Carlo methods
- These nonlinear Markov chain models and their mean field particle interpretation can be extended to time non homogeneous models on general measurable state spaces ( including transition states, path spaces and random excursion spaces ) and continuous time models.
- The mean field particle interpretation of this Feynman-Kac model is defined by sampling sequentially " N " conditionally independent random variables \ xi ^ { ( N, i ) } _ { n + 1 } with probability distribution
- In 1984 Carlo Rubbia and Simon van der Meer were awarded the Nobel Prize " for their decisive contributions to the large project, which led to the discovery of the field particles W and Z, communicators of weak interaction"
- These flows of probability distributions can always be interpreted as the distributions of the random states of a Markov process whose transition probabilities depend on the distributions of the current random states ( see mean field particle techniques rely on sequential interacting samples.
- The origins of Quantum Monte Carlo methods are often attributed to Enrico Fermi and Robert Richtmyer who developed in 1948 a mean field particle interpretation of neutron-chain reactions, but the first heuristic-like and genetic type particle algorithm ( a . k . a.
- The first uniform convergence results with respect to the time parameter for mean field particle models were developed in the end of the 1990s by Pierre Del Moral and Alice Guionnet for interacting jump type processes, and by Florent Malrieu for nonlinear diffusion type processes.
- The third term contains the effects of cross-field particle drifts, including the curvature drift, the grad-B drift, and the lowest order E-cross-B drift . The fourth term represents the nonlinear effect of the perturbed \ vec { E } \ times \ vec { B } drift interacting with the distribution function perturbation.
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