Mann,N. R., Fertig,K. W. · Defense Technical Information Center (DTIC) · 1975
A prediction-interval approach was derived for determining Gaussian sampling plans for finite lots required to be of very high quality. The plans are such that a lot is rejected if a single defective item is found in the sample. No loss function is required for the implementation of the plans, so they may be used in any situation in which the manufacturing process is Gaussian and lots are small (less than 100 items) and required to be of high quality. The savings in sample size over traditional attribute plans or variables plans assuming an infinite lot size is considerable.
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Merlise Clyde, Joyee Ghosh, Michael L. Littman et al. · 2016
All in-text\treferences\tunderlined\tin\tblue\tare\tlinked\tto\tpublications\ton\tResearchGate, letting you\taccess\tand\tread\tthem\timmediately.
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Bozhena Bidyuk, Rina Dechter · 2008
The paper presents a new sampling methodology for Bayesian networks that samples only a subset of variables and applies exact inference to the rest. Cutset sampling is a network structure-exploiting application of the Rao-Blackwellisation principle to sampling in Bayesian networks. It improves convergence by exploiting memory-based inference algorithms. It can also be viewed as an anytime approximation of exact cutset-conditioning algorithm (Pearl, 1988). Cutset sampling can be implemented efficiently when the sampled variables constitute a loop-cutset of the Bayesian network and, more generally, when the induced width of the network’s graph conditioned on the observed sampled variables is bounded by a constant w. We demonstrate empirically the benefit of this scheme on a range of benchmarks. 1.
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Kumar, Ankit · IDEALS (University of Illinois Urbana-Champaign) · 2020
We address the problem of sampling in attributed networks. While uniform sampling is a task independent sampling method, in real-world, this is often difficult to implement as it requires random access to all the nodes of graph. Link tracing sampling methods such as random Walk, expansion sampling overcome this problem, however they do not utilize the information provided by attributes of the nodes and just use the topology of the graph. We propose a network sampling method which is task independent and utilizes the node attributes. Our approach is based on introducing maximum unfamiliarity in each sampling step and it uses Bayesian approach to asses the familiarity of neighboring nodes with respect to the current sample.
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Jung-beom Lee, Dr. Kaan Ozbay, Jung-beom Lee · 2008
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Yajuan Si, See Profile, Yajuan Si et al. · 2014
Abstract. It has historically been a challenge to perform Bayesian inference in a design-based survey context. The present paper develops a Bayesian model for sampling inference in the presence of inverse-probability weights. We use a hierar-chical approach in which we model the distribution of the weights of the nonsam-pled units in the population and simultaneously include them as predictors in a nonparametric Gaussian process regression. We use simulation studies to evaluate the performance of our procedure and compare it to the classical design-based es-timator. We apply our method to the Fragile Family and Child Wellbeing Study. Our studies find the Bayesian nonparametric finite population estimator to be more robust than the classical design-based estimator without loss in efficiency, which works because we induce regularization for small cells and thus this is a way of automatically smoothing the highly variable weights.
Cited by 0
Shi Chen, Zhiyan Ding, Qin Li · Modeling and simulation in science, engineering & technology · 2024
Cited by 1
R. P. Treder, J. Sedransk · Journal of the American Statistical Association · 1996
Abstract We propose a sequential two-phase sample design to accommodate applications where conventional two-phase sampling cannot be used. First, we derive the optimal, the optimal myopic, and several approximate optimal myopic sequential decision procedures for subsampling a first-phase sample. Then we compare our procedure to that of the optimal (nonsequential) conventional two-phase sample design. In our application, the objective is inference about the age distribution of a population of fish using data on covariates (e.g., length, weight) obtained from all members of the first-phase sample and data on age obtained from the fish in the second-phase subsample.
Cited by 1
Yang Zhao, Jianyi Zhang, Changyou Chen · Proceedings of the AAAI Conference on Artificial Intelligence · 2019
Scalable Bayesian sampling is playing an important role in modern machine learning, especially in the fast-developed unsupervised-(deep)-learning models. While tremendous progresses have been achieved via scalable Bayesian sampling such as stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD), the generated samples are typically highly correlated. Moreover, their sample-generation processes are often criticized to be inefficient. In this paper, we propose a novel self-adversarial learning framework that automatically learns a conditional generator to mimic the behavior of a Markov kernel (transition kernel). High-quality samples can be efficiently generated by direct forward passes though a learned generator. Most importantly, the learning process adopts a self-learning paradigm, requiring no information on existing Markov kernels, e.g., knowledge of how to draw samples from them. Specifically, our framework learns to use current samples, either from the generator or pre-provided training data, to update the generator such that the generated samples progressively approach a target distribution, thus it is called self-learning. Experiments on both synthetic and real datasets verify advantages of our framework, outperforming related methods in terms of both sampling efficiency and sample quality.
Cited by 6Open access
Tim Hesterberg · The IMA volumes in mathematics and its applications · 1991
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John Skilling · 2008
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Erik Bodin · Bristol Research (University of Bristol)
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Tin Lai, Philippe Morere, Fábio Ramos et al. · arXiv (Cornell University) · 2019
Sampling-based planning is the predominant paradigm for motion planning in robotics. Most sampling-based planners use a global random sampling scheme to guarantee probabilistic completeness. However, most schemes are often inefficient as the samples drawn from the global proposal distribution, and do not exploit relevant local structures. Local sampling-based motion planners, on the other hand, take sequential decisions of random walks to samples valid trajectories in configuration space. However, current approaches do not adapt their strategies according to the success and failures of past samples. In this work, we introduce a local sampling-based motion planner with a Bayesian learning scheme for modelling an adaptive sampling proposal distribution. The proposal distribution is sequentially updated based on previous samples, consequently shaping it according to local obstacles and constraints in the configuration space. Thus, through learning from past observed outcomes, we maximise the likelihood of sampling in regions that have a higher probability to form trajectories within narrow passages. We provide the formulation of a sample-efficient distribution, along with theoretical foundation of sequentially updating this distribution. We demonstrate experimentally that by using a Bayesian proposal distribution, a solution is found faster, requiring fewer samples, and without any noticeable performance overhead.
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Yajuan Si, Natesh S. Pillai, Andrew Gelman · Bayesian Analysis · 2015
It has historically been a challenge to perform Bayesian inference in a design-based survey context. The present paper develops a Bayesian model for sampling inference in the presence of inverse-probability weights. We use a hierarchical approach in which we model the distribution of the weights of the nonsampled units in the population and simultaneously include them as predictors in a nonparametric Gaussian process regression. We use simulation studies to evaluate the performance of our procedure and compare it to the classical design-based estimator. We apply our method to the Fragile Family and Child Wellbeing Study. Our studies find the Bayesian nonparametric finite population estimator to be more robust than the classical design-based estimator without loss in efficiency, which works because we induce regularization for small cells and thus this is a way of automatically smoothing the highly variable weights.
Cited by 43Open access
Haotian Chen · Figshare · 2017
This thesis aims to investigate three main topics, which are the density-based multiple variance ratio test, Bayesian estimation of partially linear models, and Bayesian estimation of Tobit models. All three topics involve a kernel density estimator whose performance is mainly determined by the choice of bandwidth. Variance ratio testing methods have been extensively used in testing for random walk hypothesis in financial markets. In Chapter 3 of this thesis, we propose a density-based multiple variance ratio test, and the testing procedure is based on the work of King, Zhang and Akram (2011). The proposed variance ratio test is constructed based on the joint density of a vector of variance ratio statistics over multiple holding periods. The joint density is estimated via a multivariate kernel estimator whose bandwidth matrix (or vector) is sampled through an Markov chain Monte Carlo (MCMC) algorithm. The Monte Carlo simulation results show that our proposed variance ratio test has a better power performance than some other conventional multiple variance ratio tests in small samples with little size distortion. In an application to the S&P500 index and FTSE100 index during the sub-prime crisis period, we find evidence that the random walk hypothesis is clearly rejected in the U.S. and U.K. stock markets. In Chapter 4, a major issue in the estimation of a partially linear model is how to estimate the linear coefficients and smoothing parameter simultaneously. We present an MCMC algorithm for such a purpose in a partially linear model, where the nonlinear component is modeled by the kernel estimator. Using the kernel-form error density approach proposed by Zhang and King (2013), we approximate the unknown error density by a kernel-form error density. The proposed Bayesian approach is nonparametric in the sense that it does not require any parametric assumption on the error distribution. We derive the posterior density of the parameters, from which parameters can be sampled using an MCMC algorithm. The Monte Carlo simulation results show that the proposed Bayesian sampling method outperforms two conventional least squares estimators in situations where the errors are assumed to follow a Laplace distribution or a mixture normal distribution. The convergence diagnostics show that the proposed sampling algorithm achieves a very good mixing performance. In an application to the gasoline consumption data in the U.S., we find that gasoline consumption is negatively related to gasoline price, while a nonlinear positive relationship exists between gasoline consumption and household income. A major issue in the estimation of a standard Tobit model is how to construct the likelihood function when the error distribution is unknown. In Chapter 5, we construct the likelihood by using the ideas of the kernel-form error density Zhang and King (2013) and data augmentation tanner and wong (1987). We present an MCMC algorithm for estimating the parameters and simulating the latent variables in a standard Tobit model. The Monte Carlo simulation results show that the proposed Bayesian sampling approach produces more accurate estimates than the semiparametric maximum likelihood estimation method proposed by cosslett (2004) and the Bayesian sampling method proposed by Chib (1992) when the errors are assumed to follow a normal, Student-t, Laplace or mixture normal distribution. Another issue with a standard Tobit model is that some relationships between variables cannot be measured parametrically. To deal with this issue, we develop a partially linear Tobit model with a kernel-form error density. In such a model, we construct the likelihood and derive the posterior density of the parameters, from which parameters can be sampled by using an MCMC algorithm. The Monte Carlo simulation results show that the proposed Bayesian sampling method produces reasonable estimates of parameters. The convergence diagnostics show that the proposed sampling algorithms achieve a very good mixing performance. With an application to the U.S. women's income data, we find that women's income is positively related to education level, while it is negatively related to age and number of children under age six. We also find a nonlinear positive relationship between women's income and working experience.
Cited by 0Open access
John Skilling · 2007
Abstract Nested sampling is a simple and general algorithm for directly computing the evidence Z (aka marginal likelihood) in multi-dimensional inference. It yields a Bayesian probability distribution Pr(Z) from which a central estimate and its uncertainty follow. Samples from the posterior distribution are available as a by-product with no extra computation. The method works by sampling proportionally to the prior, though constrained within a ‘nested’ sequence of progressively-higher likelihood contours. Exploration depends only on the shape of these contours and not on the associated values, so that nested sampling is invariant to monotonic re-labelling of likelihood. Thus, in multiphase applications, nested sampling bridges between different phases in a way denied to any thermal method, promising considerable extra scope. Although presented here in a Bayesian context, nested sampling is equally applicable to numerical integration of general positive functions in many dimensions.
Cited by 5
斌 刘, Bang Wang · arXiv (Cornell University) · 2022
How to sample high quality negative instances from unlabeled data, i.e., negative sampling, is important for training implicit collaborative filtering and contrastive learning models. Although previous studies have proposed some approaches to sample informative instances, few has been done to discriminating false negative from true negative for unbiased negative sampling. On the basis of our order relation analysis of negatives' scores, we first derive the class conditional density of true negatives and that of false negatives. We next design a Bayesian classifier for negative classification, from which we define a model-agnostic posterior probability estimate of an instance being true negative as a quantitative negative signal measure. We also propose a Bayesian optimal sampling rule to sample high-quality negatives. The proposed Bayesian Negative Sampling (BNS) algorithm has a linear time complexity. Experimental studies validate the superiority of BNS over the peers in terms of better sampling quality and better recommendation performance.
Cited by 0Open access
Yang Zhao, Jianyi Zhang, Changyou Chen · arXiv (Cornell University) · 2018
Scalable Bayesian sampling is playing an important role in modern machine learning, especially in the fast-developed unsupervised-(deep)-learning models. While tremendous progresses have been achieved via scalable Bayesian sampling such as stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD), the generated samples are typically highly correlated. Moreover, their sample-generation processes are often criticized to be inefficient. In this paper, we propose a novel self-adversarial learning framework that automatically learns a conditional generator to mimic the behavior of a Markov kernel (transition kernel). High-quality samples can be efficiently generated by direct forward passes though a learned generator. Most importantly, the learning process adopts a self-learning paradigm, requiring no information on existing Markov kernels, e.g., knowledge of how to draw samples from them. Specifically, our framework learns to use current samples, either from the generator or pre-provided training data, to update the generator such that the generated samples progressively approach a target distribution, thus it is called self-learning. Experiments on both synthetic and real datasets verify advantages of our framework, outperforming related methods in terms of both sampling efficiency and sample quality.
Cited by 0Open access
Murali Niverthi And Dipak K. Dey, Murali Niverthi, Dipak K. Dey · 2007
This paper demonstrates the use of Bayesian methods in Acceptance Sampling for items which are categorized according to their attributes as defective or non defective, i.e binary (0-1) data. The main thrust of the paper is to account for the possibility of dependence among the items of a sample. An attempt has been made to incorporate the dependence structure in the acceptance sampling scheme. Two different Bayesian models have been proposed with a discussion of their advantages and shortcomings. Some of the common Acceptance Sampling measures for attributes like the Average Outgoing Quality (AOQ), Average Total Inspection (ATI), sampling costs, Operating Characteristic (OC) curve have been examined from a Bayesian viewpoint. Computation of such acceptance sampling measures are provided on simulated data using a sampling based approach based on Markov Chain Monte Carlo methods. Introduction In Quality Control techniques employed in industry, decisions to accept or reject lots are base...
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R. P. Treder, J. Sedransk · Journal of the American Statistical Association · 1996
Abstract We propose a sequential two-phase sample design to accommodate applications where conventional two-phase sampling cannot be used. First, we derive the optimal, the optimal myopic, and several approximate optimal myopic sequential decision procedures for subsampling a first-phase sample. Then we compare our procedure to that of the optimal (nonsequential) conventional two-phase sample design. In our application, the objective is inference about the age distribution of a population of fish using data on covariates (e.g., length, weight) obtained from all members of the first-phase sample and data on age obtained from the fish in the second-phase subsample.
Cited by 3