Adaptive frequency sampling using linear Bayesian vector fitting

Adaptive Sampling using Support Vector Machines

For these reasons, there is currently an increasing interest in the development of dynamic PRA methodologies [2, 3, 4, 5] since they can be used to address the deficiencies of the conventional methods listed above. However, while dynamic methodologies have distinct advantages over conventional methods, there is no general agreement about the need for dynamic methods due to the computational cha...

A tutorial on fitting Bayesian linear mixed models using Stan

With the arrival of the R packages nlme and lme4, linear mixed models (LMMs) have come to be widely used in psychology, cognitive science, and related areas. In this tutorial, we provide a practical introduction to fitting LMMs in a Bayesian framework using the probabilistic programming language Stan. Although the Bayesian framework has several important advantages, specifying a Bayesian model ...

Frequency Response Identification Using Exact Adaptive Frequency Sampling Filters

لا تانيعلا تاذ ةيفيكتلا تاحشرملا مدختست ةيبذبذت FSF ةبذبذلا لاجم يف فيكتلا ءارجا دنع ةصاخ ةروصب . هذه ليوحت ريدقتب موقت تاحشرملا Fourier ددعب عطقتملا N نم هسفن ددعلا تاذ ةيزفحتلا ةباجتسلال تانيعلا نم تانيعلا . ليدبآ ةماع ةروصب اهمادختسا نكمي ،عيرسو عقوتم يبراقت لدعم تاذ تاحشرملا هذه نوكل اًرظن ملل ةفلتخم تاقيبطت يف ةيديلقتلا تاحشر . ةيفيكتلا تاحشرملا نم اًديدج اًعون ثحبلا اذه يف حرتقن FSF يذلا نيب...

Bayesian Analysis of Vector ARMA Models using Gibbs Sampling

Adaptive Importance Sampling Using Probabilistic Classification Vector Machines

This abstract presents the basic idea of a new adaptive methodology for reliability assessment using probabilistic classification vector machines (PCVMs) [1], a variant of support vector machines (SVMs) [2, 3]. The proposed method is pivoted around two principal concepts definition of an explicit failure boundary and its variability using PCVMs, and importance sampling (IS) [4–6]. The proposed ...