1. The Randomness Recycler versus Markov chains At the heart of the Monte Carlo approach is the ability to sample from distributions that are in general very difficult to describe completely. For instance, the distribution might have an unknown normalizing constant which might require exponential time to compute. In these situations, in lieu of an exact approach, Markov chains are often employe...

After a discussion on the historic evolvement of the concept of representative sampling in official statistics, we propose a prediction approach to it. 1 The birth of representative method The word birth should not be taken literally, as there are many fathers and few (if any) mothers involved in the process. As a matter of fact, social and demographic statistics emerged as a result of partial ...

Introduction Conclusions References

This paper intends to serve as an educational introduction to sampling theory. Basically, sampling theory deals with the reconstruction of functions (signals) through their values (samples) on an appropriate sequence of points by means of sampling expansions involving these values. In order to obtain such sampling expansions in a unified way, we propose an inductive procedure leading to various...

A sampling-based framework for finding the optimal representation of a finite energy optical field using a finite number of bits is presented. For a given bit budget, we determine the optimum number and spacing of the samples in order to represent the field with as low error as possible. We present the associated performance bounds as trade-off curves between the error and the cost budget. In c...

Relationships that exist between the classical, Shannontype, and geometric-based approaches to sampling are investigated. Some aspects of coding and communication through a Gaussian channel are considered. In particular, a constructive method to determine the quantizing dimension in Zador’s theorem is provided. A geometric version of Shannon’s Second Theorem is introduced. Applications to Pulse...

Calibration is commonly used in survey sampling to include auxiliary information to increase the precision of the estimates of population parameter. In this paper, we newly propose various calibration approach ratio estimators and derive the estimator of the variance of the calibration approach ratio estimators in stratified sampling. r 2006 Elsevier B.V. All rights reserved.

Clustering methods are machine-learning algorithms that can be used to easily select the most representative samples within a huge program trace. k-means is a popular clustering method for sampling. While k-means performs well, it has several shortcomings: (1) it depends on a random initialization, so that clustering results may vary across runs; (2) the maximal number of clusters is a user-sel...

Sparse sampling schemes have the potential to reduce image acquisition time by reconstructing a desired image from a sparse subset of measured pixels. Moreover, dynamic sparse sampling methods have the greatest potential because each new pixel is selected based on information obtained from previous samples. However, existing dynamic sampling methods tend to be computationally expensive and ther...