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About 10,000 papers for “adaptive sampling”

Adaptive Sampling

Myron Katzoff, Steve K. Thompson, George A. F. Seber · Journal of the American Statistical Association · 1997

Cited by 2

Adaptive Sampling

George A. F. Seber, Mohammad M. Salehi · Wiley StatsRef: Statistics Reference Online · 2015

Abstract Animal populations are often highly grouped. For example, fish can form large, widely scattered schools with few fish in between. Even rare species of animals may form small groups that are hard to find. Applying standard sampling methods such as simple random sampling of plots to such a population could yield little information, with most of the plots being empty. Density estimation and physiological information based on this meagre information will have poor precision. Adaptive sampling is a method of unequal probability sampling based on the simple idea that when some animals are located on a sample plot, the neighboring plots (and possibly their neighbors as well) are added to the sample; in this way, the whole group can be sampled. The scheme lends itself to a variety of modifications, for example, sampling with and without replacement, sequential and inverse sampling, stratified sampling, two‐stage sampling, and the so‐called adaptive allocation, as well as extensions to order statistics, multivariate data, incomplete detectability, and super‐population models.

Cited by 0

Adaptive Sampling

George A. F. Seber, Mohammad Salehi · Wiley StatsRef: Statistics Reference Online · 2014

Abstract Animal populations are often highly clustered. For example, fish can form large, widely scattered schools with few fish in between. Even rare species of animals may form small groups that are hard to find. Applying standard sampling methods such as simple random sampling of plots to such a population could yield little information, with most of the plots being empty. Adaptive sampling is based on the simple idea that when some animals are located on a sample plot, the neighboring plots (and possibly their neighbors as well) are added to the sample; in this way, the whole cluster can be sampled. The scheme lends itself to a variety of modifications, for example, sampling with and without replacement, sequential and inverse sampling, stratified sampling, two‐stage sampling, and so‐called adaptive allocation, as well as extensions to multivariate data and incomplete detectability.

Cited by 0

Adaptive visual sampling

Yogesh Raja · OpenGrey (Institut de l'Information Scientifique et Technique) · 2010

Various visual tasks may be analysed in the context of sampling from the visual field. In visual psychophysics, human visual sampling strategies have often been shown at a high-level to be driven by various information and resource related factors such as the limited capacity of the human cognitive system, the quality of information gathered, its relevance in context and the associated efficiency of recovering it. At a lower-level, we interpret many computer vision tasks to be rooted in similar notions of contextually-relevant, dynamic sampling strategies which are geared towards the filtering of pixel samples to perform reliable object association. In the context of object tracking, the reliability of such endeavours is fundamentally rooted in the continuing relevance of object models used for such filtering, a requirement complicated by realworld conditions such as dynamic lighting that inconveniently and frequently cause their rapid obsolescence. In the context of recognition, performance can be hindered by the lack of learned context-dependent strategies that satisfactorily filter out samples that are irrelevant or blunt the potency of models used for discrimination. In this thesis we interpret the problems of visual tracking and recognition in terms of dynamic spatial and featural sampling strategies and, in this vein, present three frameworks that build on previous methods to provide a more flexible and effective approach. Firstly, we propose an adaptive spatial sampling strategy framework to maintain statistical object models for real-time robust tracking under changing lighting conditions. We employ colour features in experiments to demonstrate its effectiveness. The framework consists of five parts: (a) Gaussian mixture models for semi-parametric modelling of the colour distributions of multicolour objects; (b) a constructive algorithm that uses cross-validation for automatically determining the number of components for a Gaussian mixture given a sample set of object colours; (c) a sampling strategy for performing fast tracking using colour models; (d) a Bayesian formulation enabling models of object and the environment to be employed together in filtering samples by discrimination; and (e) a selectively-adaptive mechanism to enable colour models to cope with changing conditions and permit more robust tracking. Secondly, we extend the concept to an adaptive spatial and featural sampling strategy to deal with very difficult conditions such as small target objects in cluttered environments undergoing severe lighting fluctuations and extreme occlusions. This builds on previous work on dynamic feature selection during tracking by reducing redundancy in features selected at each stage as well as more naturally balancing short-term and long-term evidence, the latter to facilitate model rigidity under sharp, temporary changes such as occlusion whilst permitting model flexibility under slower, long-term changes such as varying lighting conditions. This framework consists of two parts: (a) Attribute-based Feature Ranking (AFR) which combines two attribute measures; discriminability and independence to other features; and (b) Multiple Selectively-adaptive Feature Models (MSFM) which involves maintaining a dynamic feature reference of target object appearance. We call this framework Adaptive Multi-feature Association (AMA). Finally, we present an adaptive spatial and featural sampling strategy that extends established Local Binary Pattern (LBP) methods and overcomes many severe limitations of the traditional approach such as limited spatial support, restricted sample sets and ad hoc joint and disjoint statistical distributions that may fail to capture important structure. Our framework enables more compact, descriptive LBP type models to be constructed which may be employed in conjunction with many existing LBP techniques to improve their performance without modification. The framework consists of two parts: (a) a new LBP-type model known as Multiscale Selected Local Binary Features (MSLBF); and (b) a novel binary feature selection algorithm called Binary Histogram Intersection Minimisation (BHIM) which is shown to be more powerful than established methods used for binary feature selection such as Conditional Mutual Information Maximisation (CMIM) and AdaBoost.

Cited by 1

Adaptive Web Sampling

Steven K. Thompson · Biometrics · 2006

A flexible class of adaptive sampling designs is introduced for sampling in network and spatial settings. In the designs, selections are made sequentially with a mixture distribution based on an active set that changes as the sampling progresses, using network or spatial relationships as well as sample values. The new designs have certain advantages compared with previously existing adaptive and link-tracing designs, including control over sample sizes and of the proportion of effort allocated to adaptive selections. Efficient inference involves averaging over sample paths consistent with the minimal sufficient statistic. A Markov chain resampling method makes the inference computationally feasible. The designs are evaluated in network and spatial settings using two empirical populations: a hidden human population at high risk for HIV/AIDS and an unevenly distributed bird population.

Cited by 79

Bayesian Adaptive Sampling

Merlise Clyde, Joyee Ghosh, Michael L. Littman et al. · 2016

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Cited by 0

Locally Adaptive Sampling

Soheil Feizi, Vivek K Goyal, Muriel Médard · 2010

In this paper, we introduce a class of Locally Adaptive Sampling schemes. In this sampling family, time intervals between samples can be computed by using a function of previously taken samples, called a sampling function. Hence, though it is a non-uniform sampling scheme, we do not need to keep sampling times. The aim of LAS is to have the average sampling rate and the reconstruction error satisfy some requirements. We propose four different schemes of LAS. The first two are designed for deterministic signals. First, we derive a Taylor Series Expansion (TSE) sampling function, which only assumes the third derivative of the signal is bounded, but requires no other specific knowledge of the signal. Then, a Discrete Time-Valued (DTV) sampling function is proposed, where the sampling time intervals are chosen from a lattice. Next, we consider stochastic signals. We propose two sampling methods based on linear prediction filters: a Generalized Linear Prediction (GLP) sampling function, and a Linear Prediction sampling function with Side Information (LPSI). In GLP method, we only assume the signal is locally stationary. However, LPSI is specifically designed for a known signal model.

Cited by 19

Locally adaptive sampling

Feizi-Khankandi, Soheil, Goyal, Vivek K., Medard, Muriel · DSpace@MIT (Massachusetts Institute of Technology) · 2010

In this paper, we introduce a class of Locally Adaptive Sampling schemes. In this sampling family, time intervals between samples can be computed by using a function of previously taken samples, called a sampling function. Hence, though it is a non-uniform sampling scheme, we do not need to keep sampling times. The aim of LAS is to have the average sampling rate and the reconstruction error satisfy some requirements. We propose four different schemes of LAS. The first two are designed for deterministic signals. First, we derive a Taylor Series Expansion (TSE) sampling function, which only assumes the third derivative of the signal is bounded, but requires no other specific knowledge of the signal. Then, a Discrete Time-Valued (DTV) sampling function is proposed, where the sampling time intervals are chosen from a lattice. Next, we consider stochastic signals. We propose two sampling methods based on linear prediction filters: a Generalized Linear Prediction (GLP) sampling function, and a Linear Prediction sampling function with Side Information (LPSI). In GLP method, we only assume the signal is locally stationary. However, LPSI is specifically designed for a known signal model.

Cited by 0Open access

Adaptive Sampling Designs

Steven K. Thompson · Wiley series in probability and statistics · 2012

Adaptive sampling refers to sampling designs in which the procedure for selecting sites or units to be included in the sample may depend on values of the variable of interest observed during the survey. The primary purpose of adaptive sampling designs is to take advantage of population characteristics to obtain more precise estimates of population abundance or density, for a given sample size or cost, than is possible with conventional designs. Adaptive selection procedures may introduce biases into conventional estimators, so that estimators and estimators of variance that are unbiased under the adaptive designs are needed. Adaptive sampling methods offer the potential to give large increases in efficiency for some populations. Sampling designs in which the selection procedure depends on observed values of the variable of interest have been of theoretical interest to statisticians for some time. Controlled Vocabulary Terms Adaptive sampling designs; estimator; sequential sampling

Cited by 0

Adaptive distance sampling

J. H. Pollard · St Andrews Research Repository (St Andrews Research Repository) · 2002

We investigate mechanisms to improve efficiency for line and point transect surveys of clustered populations by combining the distance methods with adaptive sampling. In adaptive sampling, survey effort is increased when areas of high animal density are located, thereby increasing the number of observations. We begin by building on existing adaptive sampling techniques, to create both point and line transect adaptive estimators, these are then extended to allow the inclusion of covariates in the detection function estimator. However, the methods are limited, as the total effort required cannot be forecast at the start of a survey, and so a new fixed total effort adaptive approach is developed. A key difference in the new method is that it does not require the calculation of the inclusion probabilities typically used by existing adaptive estimators. The fixed effort method is primarily aimed at line transect sampling, but point transect derivations are also provided. We evaluate the new methodology by computer simulation, and report on surveys of harbour porpoise in the Gulf of Maine, in which the approach was compared with conventional line transect sampling. Line transect simulation results for a clustered population showed up to a 6% improvement in the adaptive density variance estimate over the conventional, whilst when there was no clustering the adaptive estimate was 1% less efficient than the conventional. For the harbour porpoise survey, the adaptive density estimate cvs showed improvements of 8% for individual porpoise density and 14% for school density over the conventional estimates. The primary benefit of the fixed effort method is the potential to improve survey coverage, allowing a survey to complete within a fixed time and effort; an important feature if expensive survey resources are involved, such as an aircraft, crew and observers.

Cited by 1

Adaptive Cluster Sampling

Steven K. Thompson · Journal of the American Statistical Association · 1990

Abstract In many real-world sampling situations, researchers would like to be able to adaptively increase sampling effort in the vicinity of observed values that are high or otherwise interesting. This article describes sampling designs in which, whenever an observed value of a selected unit satisfies a condition of interest, additional units are added to the sample from the neighborhood of that unit. If any of these additional units satisfies the condition, still more units may be added. Sampling designs such as these, in which the selection procedure is allowed to depend on observed values of the variable of interest, are in contrast to conventional designs, in which the entire selection of units to be included in the sample may be determined prior to making any observations. Because the adaptive selection procedure introduces biases into conventional estimators, several estimators are given that are design unbiased for the population mean with the adaptive cluster designs of this article; that is, the unbiasedness does not depend on any assumptions about the population. The Rao-Blackwell method is used to obtain improved unbiased estimators; because of the incompleteness of the minimal sufficient statistic, more than one of these improved estimators are obtained. Simple criteria are given determining when adaptive cluster sampling strategies are more efficient than simple random sampling of equivalent sample size. Motivation for the designs in this article is provided by a wide variety of sampling situations in fields such as ecology, geology, and epidemiology. For example, in a survey of a rare bird species, once individuals of the species are detected, additional observations at nearby sites often reveal more individuals. In a study of a contagious disease, the addition to the sample of close contacts of infected individuals reveals a higher than average incidence rate. The results and examples in this article show that adaptive cluster sampling strategies give lower variance than conventional strategies for certain types of populations and, in particular, provide an extremely effective way of sampling rare, clustered populations.

Cited by 100

ADAPTIVE SAMPLING PREQU.bNCY

Merri Tt C. Far Ren, Charles A O Phl Lips, Charles Aa Phillips · 1971

Approved for public release; distribution is unlimited. Distribution authorized to U.S. Gov't. agencies and their contractors;

Cited by 0