Timebase distortion causes nonlinear distortion of waveforms measured by sampling instruments. When such instruments are used to measure the rms amplitude of the sampled waveforms, such distortions result in errors in the measured root-mean squared (rms) values. This paper looks at the nature of the errors that result from nonrandom quantization errors in an instrument’s timebase circuit. Simul...

Conventional Monte Carlo simulations are stochastic in the sense that the acceptance of a trial move is decided by comparing a computed acceptance probability with a random number, uniformly distributed between 0 and 1. Here, we consider the case that the weight determining the acceptance probability itself is fluctuating. This situation is common in many numerical studies. We show that it is p...

Sensitivity and uncertainty analyses based on Monte Carlo sampling were undertaken for various numbers of runs of the pesticide leaching model (PELMO). Analyses were repeated 10 times with different seed numbers. The ranking of PELMO input parameters according to their influence on predictions for leaching was stable for the most influential parameters. For less influential parameters, the sens...

Representing against population with a subset of it is termed as sampling. Sampling can either be statistical or non-statistical. In statistical sampling (probability sampling technique) calculating the probability of getting any particular sample is possible. It is scientific and every element stands an equal chance of being selected. In statistical sampling, workforce, time and money highly l...

It is common practice to use weighting, poststratiication, and raking to correct for sampling and nonsampling biases and to improve eeciency of estimation in sample surveys. However, there is no standard method for computing sampling variances of estimates that use these adjustments in combination. In this paper we develop such a method, using three ideas: (1) a general notation that uniies the...

We introduce the concept of effective fraction, defined as the expected probability that a configuration from the lowest index replica successfully reaches the highest index replica during a replica exchange Monte Carlo simulation. We then argue that the effective fraction represents an adequate measure of the quality of the sampling technique, as far as swapping is concerned. Under the hypothe...

A reaction probability is required to calculate the rate constant of a diffusion-dominated reaction. Due to the complicated geometry and potentially high dimension of the reaction probability problem, it is usually solved by a Brownian dynamics simulation, also known as a random walk or path integral method, instead of solving the equivalent partial differential equation by a discretization met...

Wildlife biologists often use grid-based designs to sample animals and generate abundance estimates. Although sampling in grids is theoretically sound, in application, the method can be logistically difficult and expensive when sampling elusive species inhabiting extensive areas. These factors make it challenging to sample animals and meet the statistical assumption of all individuals having an...

As an important branch of evolutionary algorithms, an Estimation of Distribution Algorithm (EDA) uses some selected individuals to build a probability model to produce offspring by sampling the probability model, which is a process of statistically learning from the selected individuals and then probabilistically sampling the probability model. A redundancy optimization model is given and many ...

Density-based techniques seem promising for handling data uncertainty in uncertain data clustering. Nevertheless, some issues have not been addressed well in existing algorithms. In this paper, we firstly propose a novel density-based uncertain data clustering algorithm, which improves upon existing algorithms from the following two aspects: (1) it employs an exact method to compute the probabi...