Iterative Importance Sampling Algorithms for Parameter Estimation

TL;DR: A backward stochastic differential equation based probabilistic machine learning method is developed, which formulates a class of stoChastic neural networks as a stochastically optimal control problem.

TL;DR: In this paper , the adaptive neuro-fuzzy inference system with a fuzzy c-means initialization (ANFIS-FCM) was proposed to solve the Bayesian inference problem.

TL;DR: In this paper, the authors present a continuation method that entails generating a sequence of transition probability density functions from the prior to the posterior in the context of Bayesian inference for parameter estimation problems.

TL;DR: The algorithm aims to mitigate some of the hurdles faced by traditional Markov Chain Monte Carlo samplers, through constructing proposal probability densities that are both, easy to sample and that provide a better approximation to the target density than a simple Gaussian proposal distribution would.

TL;DR: Van Genuchten et al. as mentioned in this paper proposed a closed-form analytical expression for predicting the hydraulic conductivity of unsaturated soils based on the Mualem theory, which can be used to predict the unsaturated hydraulic flow and mass transport in unsaturated zone.

TL;DR: In this article, the problem of least square problems with non-linear normal equations is solved by an extension of the standard method which insures improvement of the initial solution, which can also be considered an extension to Newton's method.

TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.

TL;DR: In this article, the authors used Darcey's law to derive the equation K∇2ψ+∇K·∇ψ +g∂K/∂z=−ρsA∆ψ/∆t for the capillary conduction of liquids in porous mediums.