检索结果-内蒙古大学图书馆

Confirmation Bias in Gaussian Mixture Models

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Balanov, Amnon Bendory, Tamir Huleihel, Wasim

Confirmation bias, the tendency to interpret information in a way that aligns with one's preconceptions, can profoundly impact scientific research, leading to conclusions that reflect the researcher's hypotheses even when the observational data do not support them. This issue is especially critical in scientific fields involving highly noisy observations, such as cryo-electron microscopy. This study investigates confirmation bias in Gaussian mixture models. We consider the following experiment: A team of scientists assumes they are analyzing data drawn from a Gaussian mixture model with known signals (hypotheses) as centroids. However, in reality, the observations consist entirely of noise without any informative structure. The researchers use a single iteration of the K-means or expectation-maximization algorithms, two popular algorithms to estimate the centroids. Despite the observations being pure noise, we show that these algorithms yield biased estimates that resemble the initial hypotheses, contradicting the unbiased expectation that averaging these noise observations would converge to zero. Namely, the algorithms generate estimates that mirror the postulated model, although the hypotheses (the presumed centroids of the Gaussian mixture) are not evident in the observations. Specifically, among other results, we prove a positive correlation between the estimates produced by the algorithms and the corresponding hypotheses. We also derive explicit closed-form expressions of the estimates for a finite and infinite number of hypotheses. This study underscores the risks of confirmation bias in low signal-to-noise environments, provides insights into potential pitfalls in scientific methodologies, and highlights the importance of prudent data interpretation. Copyright © 2024, The Authors. All rights reserved.

关键词： expectation maximization algorithm

A FLEXIBLE MODEL FOR RECORD LINKAGE

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Robach, Kayané van der Pas, Stéphanie L. van de Wiel, Mark A. Hof, Michel H. Department of Epidemiology and Data Science Amsterdam UMC location Vrije Universiteit Amsterdam De Boelelaan 1117 Amsterdam1081 HV Netherlands Amsterdam Public Health Methodology Netherlands

Combining data from various sources empowers researchers to explore innovative questions, for example those raised by conducting healthcare monitoring studies. However, the lack of a unique identifier often poses challenges. Record linkage procedures determine whether pairs of observations collected on different occasions belong to the same individual using partially identifying variables (e.g. birth year, postal code). Existing methodologies typically involve a compromise between computational efficiency and accuracy. Traditional approaches simplify this task by condensing information, yet they neglect dependencies among linkage decisions and disregard the one-to-one relationship required to establish coherent links. Modern approaches offer a comprehensive representation of the data generation process, at the expense of computational overhead and reduced flexibility. We propose a flexible method, that adapts to varying data complexities, addressing registration errors and accommodating changes of the identifying information over time. Our approach balances accuracy and scalability, estimating the linkage using a Stochastic expectation Maximisation algorithm on a latent variable model. We illustrate the ability of our methodology to connect observations using large real data applications and demonstrate the robustness of our model to the linking variables quality in a simulation study. The proposed algorithm FlexRL is implemented and available in an open source R package. © 2024, CC BY.

关键词： expectation maximization algorithm

Horseshoe-type Priors for Independent Component Estimation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Datta, Jyotishka Polson, Nicholas G. Department of Statistics Virginia Tech United States Booth School of Business University of Chicago United States

Independent Component Estimation (ICE) has many applications in modern day machine learning as a feature engineering extraction method. Horseshoe-type priors are used to provide scalable algorithms that enables both point estimates via expectation-maximization (EM) and full posterior sampling via Markov Chain Monte Carlo (MCMC) algorithms. Our methodology also applies to flow-based methods for nonlinear feature extraction and deep learning. We also discuss how to implement conditional posteriors and envelope-based methods for optimization. Through this hierarchy representation, we unify a number of hitherto disparate estimation procedures. We illustrate our methodology and algorithms on a numerical example. Finally, we conclude with directions for future *** Codes 62F15, 62H25, 68T07 © 2024, CC BY-NC-ND.

关键词： expectation maximization algorithm

Bayesian Variable Selection and Sparse Estimation for High-Dimensional Graphical Models

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Chakravarti, Anwesha Narisetty, Naveen N. Liang, Feng University of Illinois at Urbana Champaign Department of Statistics United States

We introduce a novel Bayesian approach for both covariate selection and sparse precision matrix estimation in the context of high-dimensional Gaussian graphical models involving multiple responses. Our approach provides a sparse estimation of the three distinct sparsity structures: the regression coefficient matrix, the conditional dependency structure among responses, and between responses and covariates. This contrasts with existing methods, which typically focus on any two of these structures but seldom achieve simultaneous sparse estimation for all three. A key aspect of our method is that it leverages the structural sparsity information gained from the presence of irrelevant covariates in the dataset to introduce covariate-level sparsity in the precision and regression coefficient matrices. This is achieved through a Bayesian conditional random field model using a hierarchical spike and slab prior setup. Despite the non-convex nature of the problem, we establish statistical accuracy for points in the high posterior density region, including the maximum-a-posteriori (MAP) estimator. We also present an efficient expectation-maximization (EM) algorithm for computing the estimators. Through simulation experiments, we demonstrate the competitive performance of our method, particularly in scenarios with weak signal strength in the precision matrices. Finally, we apply our method to a bike-share dataset, showcasing its predictive performance. © 2024, CC BY-NC-SA.

关键词： expectation maximization algorithm

Mini-batch Submodular maximization

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Schwartzman, Gregory JAIST Japan

We present the first mini-batch algorithm for maximizing a non-negative monotone decomposable submodular function, (Equation presented), under a set of constraints. We consider two sampling approaches: uniform and weighted. We first show that mini-batch with weighted sampling improves over the state of the art sparsifier based approach both in theory and in practice. Surprisingly, our experimental results show that uniform sampling is superior to weighted sampling. However, it is impossible to explain this using worst-case analysis. Our main contribution is using smoothed analysis to provide a theoretical foundation for our experimental results. We show that, under very mild assumptions, uniform sampling is superior for both the mini-batch and the sparsifier approaches. We empirically verify that these assumptions hold for our datasets. Uniform sampling is simple to implement and has complexity independent of N, making it the perfect candidate to tackle massive real-world datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： expectation maximization algorithm

BAYESIAN EXPERIMENTAL DESIGN VIA CONTRASTIVE DIFFUSIONS

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Iollo, Jacopo Heinkelé, Christophe Alliez, Pierre Forbes, Florence Université Grenoble Alpes Inria CNRS G-INP France Cerema Endsum Strasbourg France Université Côte d’Azur Inria France

Bayesian Optimal Experimental Design (BOED) is a powerful tool to reduce the cost of running a sequence of experiments. When based on the Expected Information Gain (EIG), design optimization corresponds to the maximization of some intractable expected contrast between prior and posterior distributions. Scaling this maximization to high dimensional and complex settings has been an issue due to BOED inherent computational complexity. In this work, we introduce a pooled posterior distribution with cost-effective sampling properties and provide a tractable access to the EIG contrast maximization via a new EIG gradient expression. Diffusion-based samplers are used to compute the dynamics of the pooled posterior and ideas from bi-level optimization are leveraged to derive an efficient joint sampling-optimization loop. The resulting efficiency gain allows to extend BOED to the well-tested generative capabilities of diffusion models. By incorporating generative models into the BOED framework, we expand its scope and its use in scenarios that were previously impractical. Numerical experiments and comparison with state-of-the-art methods show the potential of the approach. © 2024, CC BY-SA.

关键词： expectation maximization algorithm

Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Qin, Chao Russo, Daniel Columbia University United States

Practitioners conducting adaptive experiments often encounter two competing priorities: maximizing total welfare (or 'reward') through effective treatment assignment and swiftly concluding experiments to implement population-wide treatments. Current literature addresses these priorities separately, with regret minimization studies focusing on the former and bestarm identification research on the latter. This paper bridges this divide by proposing a unified model that simultaneously accounts for within-experiment performance and post-experiment outcomes. We provide a sharp theory of optimal performance in large populations that not only unifies canonical results in the literature but also uncovers novel insights. Our theory reveals that familiar algorithms, such as the recently proposed top-two Thompson sampling algorithm, can optimize a broad class of objectives if a single scalar parameter is appropriately adjusted. In addition, we demonstrate that substantial reductions in experiment duration can often be achieved with minimal impact on both within-experiment and post-experiment regret. Copyright © 2024, The Authors. All rights reserved.

关键词： expectation maximization algorithm

Research on Network Self-protection System Model and Its Security Event Correlation Engine

学校读者我要写书评

暂无评论

Research on Network Self-protection System Model and Its Sec...

2010 International Conference on Services Science, Management and Engineering (SSME 2010)

作者： Hongyu Yang School of Computer Science and Technology Civil Aviation University of China Tianjin 300300,China Dongming Wang School of Computer Science and Technology Civil Aviation University of China Tianjin 300300,China Chuangxi Chen Dep.of ground maintenance China Southern Airlines Co.Ltd. Guangzhou 510406,China

Manual analysis of security events inevitably results in a large number of attacks can not being detected timely This paper presents a new network self-protection system model,gives its architecture and designs its information security event correlative analysis *** the correlation engine,the expectation maximization(EM) algorithm was designed to solve the problem by lack of security *** the bucket elimination algorithm was used to enhance the computational efficiency of Bayesian *** experimental results demonstrate that the reasoning speed of the correlation engine fulfills the need of network protection against unknown attacks.

关键词： self-protection correlation engine Bayesian network bucket elimination algorithm expectation maximization algorithm

Policy Choice in Time Series by Empirical Welfare maximization

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Kitagawa, Toru Wang, Weining Xu, Mengshan Department of Economics Brown University United States Department of Economics University College London United Kingdom Faculty of economics and business University of Groningen Netherlands Department of Economics University of Mannheim Germany

This paper develops a novel method for policy choice in a dynamic setting where the available data is a multi-variate time series. Building on the statistical treatment choice framework, we propose Time-series Empirical Welfare maximization (T-EWM) methods to estimate an optimal policy rule by maximizing an empirical welfare criterion constructed using nonparametric potential outcome time series. We characterize conditions under which T-EWM consistently learns a policy choice that is optimal in terms of conditional welfare given the time-series history. We derive a nonasymptotic upper bound for conditional welfare regret. To illustrate the implementation and uses of T-EWM, we perform simulation studies and apply the method to estimate optimal restriction rules against Covid-19. © 2022, CC BY.

关键词： expectation maximization algorithm