Probability Weights

Views Updated: Dec 15, 2025

Abbreviation

Pronunciation

/ˌprɑbəˈbɪləˌti weɪts/

A particle set, which is randomly sampled from probability function and has corresponding weights, is introduced to approach the posterior distribution. Therefore it can handle nonlinear and non-Gaussian model without any limits.
Training this NN detector at some specified probability of false alarm and then adjusting the weights of the bias nodes, we can acquire the test statistics.
And use the principle of multiplication to calculate the probability of indicators at all levels of the portfolio weights, as detailed in Appendix 4.3.
The Monte Carlo particle filter algorithms in this paper use the concepts of sequential importance sampling. The base idea of particle filter is the approximation of relevant probability distributions using a set of discrete random samples with associated weights.
In order to increase the detection speed, by using mixing particle sets to express the posterior probability distribution and adopting the Monte Carlo numerical methods to calculate the mixing weights, the fast fault detection algorithm is proposed based on the estimate window method.

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