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The majority of experiments in fundamental science today are designed to be multi-purpose: their aim is not simply to measure a single physical quantity or process, but rather to enable increased precision in the measurement of a number of…

Sub-sampling is a common and often effective method to deal with the computational challenges of large datasets. However, for most statistical models, there is no well-motivated approach for drawing a non-uniform subsample. We show that the…

Machine Learning · Statistics 2017-09-07 Daniel Ting , Eric Brochu

This paper proposes a new approach to construct high quality space-filling sample designs. First, we propose a novel technique to quantify the space-filling property and optimally trade-off uniformity and randomness in sample designs in…

This paper analyses the effect of preferential sampling in Geostatistics when the choice of new sampling locations is the main interest of the researcher. A Bayesian criterion based on maximizing utility functions is used. Simulated studies…

Statistics Theory · Mathematics 2015-09-23 Gustavo da Silva Ferreira , Dani Gamerman

A Bayesian design is given by maximising an expected utility over a design space. The utility is chosen to represent the aim of the experiment and its expectation is taken with respect to all unknowns: responses, parameters and/or models.…

Methodology · Statistics 2019-01-16 Antony M. Overstall , James M. McGree

The aim of this paper is twofold. First, three theoretical principles are formalized: randomization, overrepresentation and restriction. We develop these principles and give a rationale for their use in choosing the sampling design in a…

Methodology · Statistics 2016-12-16 Yves Tillé , Matthieu Wilhelm

Human decision-makers often receive assistance from data-driven algorithmic systems that provide a score for evaluating objects, including individuals. The scores are generated by a function (mechanism) that takes a set of features as input…

Machine Learning · Computer Science 2019-11-25 Abolfazl Asudeh , H. V. Jagadish

Data collection costs can vary widely across variables in data science tasks. Two-phase designs can be employed to save data collection costs. This paper considers the two-phase studies where inexpensive variables are collected for all…

Methodology · Statistics 2025-12-04 Ruoyu Wang , Qihua Wang , Wang Miao

Survey sampling plays an important role in the efficient allocation and management of resources. The essence of survey sampling lies in acquiring a sample of data points from a population and subsequently using this sample to estimate the…

Methodology · Statistics 2024-01-29 Jonne Pohjankukka , Sakari Tuominen , Jukka Heikkonen

In this short paper, we describe an efficient numerical solver for the optimal sampling problem considered in "Designing Sampling Schemes for Multi-Dimensional Data". An implementation may be found on…

Signal Processing · Electrical Eng. & Systems 2021-11-11 Filip Elvander , Johan Swärd , Andreas Jakobsson

We examine a fundamental problem that models various active sampling setups, such as network tomography. We analyze sampling of a multivariate normal distribution with an unknown expectation that needs to be estimated: in our setup it is…

Machine Learning · Statistics 2012-08-14 Assaf Hallak , Shie Mannor

Sampling techniques are used in many fields, including design of experiments, image processing, and graphics. The techniques in each field are designed to meet the constraints specific to that field such as uniform coverage of the range of…

Machine Learning · Computer Science 2023-06-08 Chandrika Kamath

We consider the problem of deciding on sampling strategy, in particular sampling design. We propose a risk measure, whose minimizing value guides the choice. The method makes use of a superpopulation model and takes into account uncertainty…

Methodology · Statistics 2020-07-06 Edgar Bueno , Dan Hedlin

Nowadays, the numerical models of real-world structures are more precise, more complex and, of course, more time-consuming. Despite the growth of a computational effort, the exploration of model behaviour remains a complex task. The…

Computational Engineering, Finance, and Science · Computer Science 2014-10-17 Eliska Janouchova , Anna Kucerova

Bayesian experimental design involves the optimal allocation of resources in an experiment, with the aim of optimising cost and performance. For implicit models, where the likelihood is intractable but sampling from the model is possible,…

Machine Learning · Statistics 2019-02-26 Steven Kleinegesse , Michael Gutmann

The maximum entropy principle can be used to assign utility values when only partial information is available about the decision maker's preferences. In order to obtain such utility values it is necessary to establish an analogy between…

Statistical Finance · Quantitative Finance 2009-11-13 Andreia Dionisio , A. Heitor Reis

This article studies the benefits of using spatially randomized experimental designs which partition the experimental area into distinct, non-overlapping units with treatments assigned randomly. Such designs offer improved policy evaluation…

Statistics Theory · Mathematics 2025-11-18 Ying Yang , Chengchun Shi , Fang Yao , Shouyang Wang , Hongtu Zhu

Subsampling is an efficient method to deal with massive data. In this paper, we investigate the optimal subsampling for linear quantile regression when the covariates are functions. The asymptotic distribution of the subsampling estimator…

Numerical Analysis · Mathematics 2022-05-06 Qian Yan , Hanyu Li , Chengmei Niu

Scientific Computing typically requires large computational needs which have been addressed with High Performance Distributed Computing. It is essential to efficiently deploy a number of complex scientific applications, which have different…

Distributed, Parallel, and Cluster Computing · Computer Science 2016-02-02 Mariza Ferro , Antonio R. Mury , Bruno Schulze

The performance of the Monte Carlo sampling methods relies on the crucial choice of a proposal density. The notion of optimality is fundamental to design suitable adaptive procedures of the proposal density within Monte Carlo schemes. This…

Computation · Statistics 2026-02-24 Fernando Llorente , Luca Martino
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