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arXiv 2025年

作者： Teisseyre, Pawel Mielniczuk, Jan Institute of Computer Science Polish Academy of Sciences Jana Kazimierza 5 Warsaw01-248 Poland Faculty of Mathematics and Information Science Warsaw University of Technology Koszykowa 75 Warsaw00-662 Poland

In many practical applications of machine learning, a discrepancy often arises between a source distribution from which labeled training examples are drawn and a target distribution for which only unlabeled data is observed. Traditionally, two main scenarios have been considered to address this issue: covariate shift (CS), where only the marginal distribution of features changes, and label shift (LS), which involves a change in the class variable’s prior distribution. However, these frameworks do not encompass all forms of distributional shift. This paper introduces a new setting, Conditional Probability Shift (CPS), which captures the case when the conditional distribution of the class variable given some specific features changes while the distribution of remaining features given the specific features and the class is preserved. For this scenario we present the Conditional Probability Shift Model (CPSM) based on modeling the class variable’s conditional probabilities using multinomial regression. Since the class variable is not observed for the target data, the parameters of the multinomial model for its distribution are estimated using the expectation-maximization algorithm. The proposed method is generic and can be combined with any probabilistic classifier. The effectiveness of CPSM is demonstrated through experiments on synthetic datasets and a case study using the MIMIC medical database, revealing its superior balanced classification accuracy on the target data compared to existing methods, particularly in situations situations of conditional distribution shift and no apriori distribution shift, which are not detected by LS-based methods. Copyright © 2025, The Authors. All rights reserved.

关键词： expectation maximization algorithm

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Multipath Component Power Delay Profile Based Joint Range and Doppler Estimation for AFDM-ISAC Systems

arXiv

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arXiv 2025年

作者： Xiao, Fangqing Li, Zunqi Slock, Dirk Communication Systems Department Eurecom France

Integrated Sensing and Communication (ISAC) systems combine sensing and communication functionalities within a unified framework, enhancing spectral efficiency and reducing costs by utilizing shared hardware components. This paper investigates multipath component power delay profile (MPCPDP)-based joint range and Doppler estimation for Affine Frequency Division Multiplexing (AFDM)-ISAC systems. The path resolvability of the equivalent channel in the AFDM system allows the recognition of Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) paths within a single pilot symbol in fast time-varying channels. We develop a joint estimation model that leverages multipath Doppler shifts and delays information under the AFDM waveform. Utilizing the MPCPDP, we propose a novel ranging method that exploits the range-dependent magnitude of the MPCPDP across its delay spread by constructing a Nakagami-m statistical fading model for MPC channel fading and correlating the distribution parameters with propagation distance in AFDM systems. This method eliminates the need for additional time synchronization or extra hardware. We also transform the nonlinear Doppler estimation problem into a bilinear estimation problem using a First-order Taylor expansion. Moreover, we introduce the expectation maximization algorithm to estimate the hyperparameters and leverage the expectation Consistent algorithm to cope with high-dimensional integration challenges. Extensive numerical simulations demonstrate the effectiveness of our MPCPDP-based joint range and Doppler estimation in ISAC systems. Copyright © 2025, The Authors. All rights reserved.

关键词： expectation maximization algorithm

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Generalized Tree-Informed Mixed Model Regression

arXiv

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arXiv 2025年

作者： Allis, Jeremiah Jin, Xin Ghosh, Riddhi Pratim Department of Mathematics and Statistics Bowling Green State University United States Department of Mathematics The University of Tampa United States

The standard regression tree method applied to observations within clusters poses both methodological and implementation challenges. Effectively leveraging these data requires methods that account for both individual-level and sample-level effects. We propose Generalized Tree-Informed Mixed Model (GTIMM), which replaces the linear fixed effect in a generalized linear mixed model (GLMM) with the output of a regression tree. Traditional parameter estimation and prediction techniques, such as the expectation-maximization algorithm, scale poorly in high-dimensional settings, creating a computational bottleneck. To address this, we employ a quasi-likelihood framework with stochastic gradient descent for optimized parameter estimation. Additionally, we establish a theoretical bound for the mean squared prediction error. The predictive performance of our method is evaluated through simulations and compared with existing approaches. Finally, we apply our model to predict country-level GDP based on trade, foreign direct investment, unemployment, inflation, and geographic region. Copyright © 2025, The Authors. All rights reserved.

关键词： expectation maximization algorithm

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Mixture models for data with unknown distributions

arXiv

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arXiv 2025年

作者： Newman, M.E.J. Center for the Study of Complex Systems University of Michigan Ann ArborMI48109 United States

We describe and analyze a broad class of mixture models for real-valued multivariate data in which the probability density of observations within each component of the model is represented as an arbitrary combination of basis functions. Fits to these models give us a way to cluster data with distributions of unknown form, including strongly non-Gaussian or multimodal distributions, and return both a division of the data and an estimate of the distributions, effectively performing clustering and density estimation within each cluster at the same time. We describe two fitting methods, one using an expectation-maximization (EM) algorithm and the other a Bayesian nonparametric method using a collapsed Gibbs sampler. The former is numerically efficient, but gives only point estimates of the probability densities. The latter is more computationally demanding but returns a full Bayesian posterior and also an estimate of the number of components. We demonstrate our methods with a selection of illustrative applications and give code implementing both algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： expectation maximization algorithm

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On the limits of some Bayesian model evaluation statistics

arXiv

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arXiv 2025年

作者： Nguyen, Hien Duy Gupta, Mayetri Westerhout, Jacob Nguyen, TrungTin Department of Mathematics and Physical Science La Trobe University Melbourne Australia Institute of Mathematics for Industry Kyushu University Fukuoka Japan School of Mathematics and Statistics University of Glasgow Glasgow United Kingdom School of Mathematics and Physics University of Queensland Brisbane Australia School of Mathematical Sciences Queensland University of Technology Brisbane Australia

Model selection and order selection problems frequently arise in statistical practice. A popular approach to addressing these problems in the frequentist setting involves information criteria based on penalized maxima of log-likelihoods for competing models. In the Bayesian context, similar criteria are employed, replacing the maxima of log-likelihoods with their posterior expectations. Despite their popularity in applications, the large-sample behavior of these criteria—such as the deviance information criterion (DIC), Bayesian predictive information criterion (BPIC), and widely-applicable Bayesian information criterion (WBIC)—has received relatively little attention. In this work, we investigate the almost sure limits of these criteria and establish novel results on posterior and generalized posterior consistency, which are of independent interest. The utility of our theoretical findings is demonstrated via illustrative technical and numerical examples. © 2025, CC BY.

关键词： expectation maximization algorithm

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expectation maximization estimates of the offspring probabilities in a class of multitype branching processes with binary family trees

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MATHEMATICAL POPULATION STUDIES 2017年第4期24卷 246-256页

作者： Daskalova, Nina Sofia Univ St Kliment Ohridski Fac Math & Informat 5 James Bourchier Blvd Sofia 1164 Bulgaria

When proliferating cells are counted in several independent colonies at some time points, the maximum likelihood estimates of the parameters of the multitype branching process are obtained trough an expectation maximization algorithm. In the case of an offspring distribution governed by a Markov branching process with binary family trees, this method, relying then on a partial knowledge of the tree, yields the same estimates as those computed with the complete knowledge of the tree.

关键词： cell proliferation expectation maximization algorithm maximum likelihood estimation multitype branching processes offspring distribution

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An expectation maximization approach to Joint Modeling of Multidimensional Ratings derived from Multiple Annotators 17

An Expectation Maximization approach to Joint Modeling of Mu...

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17th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2016)

作者： Ramakrishna, Anil Gupta, Rahul Grossman, Ruth B. Narayanan, Shrikanth S. Univ Southern Calif Signal Anal & Interpretat Lab Los Angeles CA 90007 USA Emerson Coll Dept Commun Sci & Disorders Boston MA 02116 USA

ISBN: (纸本)9781510833135

Ratings from multiple human annotators are often pooled in applications where the ground truth is hidden. Examples include annotating perceived emotions and assessing quality metrics for speech and image. These ratings are not restricted to a single dimension and can be multidimensional. In this paper, we propose an expectation-maximization based algorithm to model such ratings. Our model assumes that there exists a latent multidimensional ground truth that can be determined from the observation features and that the ratings provided by the annotators are noisy versions of the ground truth. We test our model on a study conducted on children with autism to predict a four dimensional rating of expressivity, naturalness, pronunciation goodness and engagement. Our goal in this application is to reliably predict the individual annotator ratings which can be used to address issues of cognitive load on the annotators as well as the rating cost. We initially train a baseline directly predicting annotator ratings from the features and compare it to our model under three different settings assuming: (i) each entry in the multidimensional rating is independent of others, (ii) a joint distribution among rating dimensions exists, (iii) a partial set of ratings to predict the remaining entries is available.

关键词： Multiple Annotator Modeling expectation maximization algorithm

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Multi-Robot Autonomous Exploration and Mapping Under Localization Uncertainty with expectation-maximization

Multi-Robot Autonomous Exploration and Mapping Under Localiz...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Huang, Yewei Lin, Xi Englot, Brendan Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (纸本)9798350384581;9798350384574

We propose an autonomous exploration algorithm designed for decentralized multi-robot teams, which takes into account map and localization uncertainties of range-sensing mobile robots. Virtual landmarks are used to quantify the combined impact of process noise and sensor noise on map uncertainty. Additionally, we employ an iterative expectation-maximization inspired algorithm to assess the potential outcomes of both a local robot's and its neighbors' next-step actions. To evaluate the effectiveness of our framework, we conduct a comparative analysis with state-of-the-art algorithms. The results of our experiments show the proposed algorithm's capacity to strike a balance between curbing map uncertainty and achieving efficient task allocation among robots.

关键词： expectation maximization algorithm

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expectation maximization-based likelihood inference for flexible cure rate models with Weibull lifetimes

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STATISTICAL METHODS IN MEDICAL RESEARCH 2016年第4期25卷 1535-1563页

作者： Balakrishnan, Narayanaswamy Pal, Suvra McMaster Univ Dept Math & Stat Hamilton ON L8S 4K1 Canada King Abdulaziz Univ Dept Stat Jeddah Saudi Arabia

Recently, a flexible cure rate survival model has been developed by assuming the number of competing causes of the event of interest to follow the Conway-Maxwell-Poisson distribution. This model includes some of the well-known cure rate models discussed in the literature as special cases. Data obtained from cancer clinical trials are often right censored and expectation maximization algorithm can be used in this case to efficiently estimate the model parameters based on right censored data. In this paper, we consider the competing cause scenario and assuming the time-to-event to follow the Weibull distribution, we derive the necessary steps of the expectation maximization algorithm for estimating the parameters of different cure rate survival models. The standard errors of the maximum likelihood estimates are obtained by inverting the observed information matrix. The method of inference developed here is examined by means of an extensive Monte Carlo simulation study. Finally, we illustrate the proposed methodology with a real data on cancer recurrence.

关键词： cure rate models long-term survivor Conway-Maxwell-Poisson distribution maximum likelihood estimators expectation maximization algorithm profile likelihood lifetime data Weibull distribution asymptotic variances Akaike's information criterion Bayesian information criterion

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Track Segment Association of Maneuvering Target based on expectation maximization 11

Track Segment Association of Maneuvering Target based on Exp...

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11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Sun, Jinping Wang, Naiyu Zhang, Zhiguo Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781538676042

Because of the high maneuverability of targets, track breakages are common in the tracking process. With the purpose of stitching the track segments and achieving better tracking results, we adopt an algorithm to stitch the track segments based on expectation maximization (EM). The EM algorithm can be used to estimate and identify the maneuvering targets' state and angular velocities simultaneously. It consists of two steps. The expectation (E) step is implemented by an extended Kalman filter (EKF) and extended Rauch-Tung-Striebel smoother (ERTSS). The maximization (M) step is implemented by genetic algorithm, which can achieve the Maximum likelihood sequence estimation for unknown parameters. Experiments show that this algorithm can achieve better tracking results. Moreover, it also exhibits good capability when estimating the unknown parameter.

关键词： expectation maximization algorithm target tracking track segment association parameter estimation

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