Adaptive sampling information

Adaptive sampling is a technique used in computational molecular biology to efficiently simulate protein folding when coupled with molecular dynamics...

sampling is also related to umbrella sampling in computational physics. Depending on the application, the term may refer to the process of sampling from...

sub-pixels, and a sample is taken from the center of each. It is fast and easy to implement. Although, due to the regular nature of sampling, aliasing can...

Bayesian Model Selection) package, the BAS (an acronym for Bayesian Adaptive Sampling) package, and the BMA package. Python: scikit-learn, a package for...

distributions, this problem can be solved using an adaptive extension (see adaptive rejection sampling), or with an appropriate change of variables with...

primary types of soil sampling are grab sampling and composite sampling. Grab sampling involves the collection of an individual sample at a specific time...

Adaptive predictive coding (APC) is a narrowband analog-to-digital conversion that uses a one-level or multilevel sampling system in which the value of...

advantage over the regular/even sampling strategy. However, adaptive ray casting upon a projection plane and adaptive sampling along each individual ray do...

"recursive rolling out and backtracking" with "adaptive" sampling choices in their Adaptive Multi-stage Sampling (AMS) algorithm for the model of Markov decision...

In statistics, stratified sampling is a method of sampling from a population which can be partitioned into subpopulations. In statistical surveys, when...

s(t) unchanged. The iterative adaptive algorithms used in adaptive filtering require only an ongoing sequence of sampling measurements at the weight inputs...

The deadband has an important application in designing perceptually adaptive sampling mechanisms for haptic data compression, which is required for transmitting...

probabilities (perhaps adapted probabilities specified by an adaptive procedure). Probability-based sampling allows design-based inference about the target population...

Demeester, T. Dhaene, K. Crombecq, (2010), “A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design," Journal of Machine Learning Research...

In statistics, cluster sampling is a sampling plan used when mutually homogeneous yet internally heterogeneous groupings are evident in a statistical population...

maintain and sample from a posterior distribution over models. As such, Thompson sampling is often used in conjunction with approximate sampling techniques...

allows aptX Adaptive at 279 kbit/s and 420 kbit/s to produce the same sound quality as aptX at 352 kbit/s and aptX HD at 576 kbit/s. aptX Adaptive supports...

reconstruction and gradient reversal. This is then followed by a contrast adaptive sharpening pass (RCAS) to reintroduce detail into the final image. (see...

similar function or use adaptive routines such as stratified sampling, recursive stratified sampling, adaptive umbrella sampling or the VEGAS algorithm...

Slice sampling is a type of Markov chain Monte Carlo algorithm for pseudo-random number sampling, i.e. for drawing random samples from a statistical distribution...

on. In many contexts, only one sample is observed, but the sampling distribution can be found theoretically. Sampling distributions are important in statistics...

statistics, multistage sampling is the taking of samples in stages using smaller and smaller sampling units at each stage. Multistage sampling can be a complex...

Simulation-based methods: Monte Carlo simulations, importance sampling, adaptive sampling, etc. General surrogate-based methods: In a non-instrusive approach...

complexity of the optimization algorithms, almost all adaptive filters are digital filters. Adaptive filters are required for some applications because some...

perform a Monte Carlo integration, such as uniform sampling, stratified sampling, importance sampling, sequential Monte Carlo (also known as a particle...