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  2. Importance sampling - Wikipedia

    en.wikipedia.org/wiki/Importance_sampling

    Importance sampling is a variance reduction technique that can be used in the Monte Carlo method.The idea behind importance sampling is that certain values of the input random variables in a simulation have more impact on the parameter being estimated than others.

  3. Subset simulation - Wikipedia

    en.wikipedia.org/wiki/Subset_simulation

    Subset simulation [1] is a method used in reliability engineering to compute small (i.e., rare event) failure probabilities encountered in engineering systems. The basic idea is to express a small failure probability as a product of larger conditional probabilities by introducing intermediate failure events.

  4. Template:Cheatsheet - Wikipedia

    en.wikipedia.org/wiki/Template:Cheatsheet

    '''bold''' ''italics'' <sup>superscript</sup> <sub>superscript</sub> → bold: → italics: → superscript → subscript <s>strikeout</s> <u>underline</u> <big>big ...

  5. GHK algorithm - Wikipedia

    en.wikipedia.org/wiki/GHK_algorithm

    The GHK algorithm (Geweke, Hajivassiliou and Keane) [1] is an importance sampling method for simulating choice probabilities in the multivariate probit model.These simulated probabilities can be used to recover parameter estimates from the maximized likelihood equation using any one of the usual well known maximization methods (Newton's method, BFGS, etc.).

  6. Particle filter - Wikipedia

    en.wikipedia.org/wiki/Particle_filter

    The sequential importance resampling technique provides another interpretation of the filtering transitions coupling importance sampling with the bootstrap resampling step. Last, but not least, particle filters can be seen as an acceptance-rejection methodology equipped with a recycling mechanism.

  7. Nested sampling algorithm - Wikipedia

    en.wikipedia.org/wiki/Nested_sampling_algorithm

    It is an alternative to methods from the Bayesian literature [3] such as bridge sampling and defensive importance sampling. Here is a simple version of the nested sampling algorithm, followed by a description of how it computes the marginal probability density Z = P ( D ∣ M ) {\displaystyle Z=P(D\mid M)} where M {\displaystyle M} is M 1 ...

  8. Control variates - Wikipedia

    en.wikipedia.org/wiki/Control_variates

    Let the unknown parameter of interest be , and assume we have a statistic such that the expected value of m is μ: [] =, i.e. m is an unbiased estimator for μ. Suppose we calculate another statistic such that [] = is a known value.

  9. File:Cheatsheet-en.pdf - Wikipedia

    en.wikipedia.org/wiki/File:Cheatsheet-en.pdf

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