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The hybrid Monte Carlo (HMC) method is a popular and rigorous method for sampling from a canonical ensemble.
A new method for sampling from a finite population that is spread in one, two or more dimensions is presented.
This is achieved by using adaptive random-walk Metropolis–Hasting method for sampling from the models parameter.
Both random walk and respondent-driven sampling (RDS) exploit social networks and may reduce biases introduced by earlier methods for sampling from hidden populations.
The objective of this paper is to propose an effective procedure for sampling from a multivariate population within the framework of Monte Carlo simulations.
First, we analyze the Modified Metropolis algorithm (MMA), an MCMC technique, which is used in SS for sampling from high-dimensional conditional distributions.
Similar(9)
Representative logarithmic grain-size diagrams are shown for samples from each sedimentary unit.
The resulting values were converted into percentages, taking the values for samples from diabetic rats without skin wounds as 100%.
We monitor samples from Geneva weekly from November to March, and every two weeks for samples from Finger Lakes and Lake Erie vineyards between mid-winter and March.
However, we did find a significant effect of distance on composition matrices for samples from rubber plantation (Mantel test, R = 0.22, P < 0.05).
The resulting values were converted into percentages, with the value for samples from diabetic rats without skin wounds set at 100%.
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