21 papers · ranked by Valyu relevance
Hideitsu Hino, Shotaro Akaho, Noboru Murata
The Expectation–Maximization (EM) algorithm is a simple meta-algorithm that has been used for many years as a methodology for statistical inference when there are missing measurements in the observed data or when the data is composed of observables and unobservables. Its general properties are well studied, and also…
Rebecca Bernemann, Barbara König, Matthias Schaffeld, Torben Weis
We consider probabilistic systems with hidden state and unobservable transitions, an extension of Hidden Markov Models (HMMs) that in particular admits unobservable ε-transitions (also called null transitions), allowing state changes of which the observer is unaware. Due to the presence of ε-loops this additional…
Andrea Bennett, Min Wang
> Abstract. The correlated binomial (CB) distribution was proposed by Luceño (Computational Statistics & Data Analysis 20, 1995, 511–520) as an alternative to the binomial distribution for the analysis of the data in the presence of correlations among events. Due to the complexity of the mixture likelihood of the…
William Ruth
The EM algorithm is a powerful tool for maximum likelihood estimation with missing data. In practice, the calculations required for the EM algorithm are often intractable. We review numerous methods to circumvent this intractability, all of which are based on Monte Carlo simulation. We focus our attention on the Monte…
Dominikus Noll
The EM algorithm assures monotone decrease of the incomplete data negative log-likelihood [30], but convergence of the iterates may fail in various ways [78]. Without coercivity iterates may escape to infinity while values converge. Even when iterates stay bounded, they may still fail to converge, cycle [76], or…
Alandra Zakkour, Cyril Perret, Yousri Slaoui, Sergio Curilef + 1 more
'Francisco Calderón'] The purpose of this paper is to propose a new algorithm based on stochastic expectation maximization (SEM) to deal with the problem of unobserved values when multiple interactions in a linear mixed-effects model (LMEM) are present. We test the effectiveness of the proposed algorithm with the…
Guo-Liang Tian, Xuanyu Liu, Yuanfan Zhao
Although the $\textit{expectation-maximization}$ (EM) algorithm is a powerful optimization tool in statistics, it can only be applied to missing/incomplete data problems or to problems with a latent-variable structure. It is well known that the introduction of latent variables (or the data augmentation) is an art…
Pengfei Hu, Wengen Gao, Yunfei Li, Minghui Wu + 4 more
'Naveen Chilamkurti' 'Jong-Hyouk Lee'] The secure operation of smart grids is closely linked to state estimates that accurately reflect the physical characteristics of the grid. However, well-designed false data injection attacks (FDIAs) can manipulate the process of state estimation by injecting malicious data into…
Kimura Takeshi, Kato, Kohtaro, Hayashi + 1 more
Takeshi Kimura 1 , ∗ Kohtaro Kato 1 , † and Masahito Hayashi2,3,4‡ 1 Department of Mathematical Informatics, Graduate School of Informatics, Nagoya University, Nagoya 464-0814, Japan 2 School of Data Science, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, 518172, China 3 International…
Tianying Feng, Li Cai
The expectation-maximization (EM) algorithm is widely used for parameter estimation in item response theory (IRT) modeling. However, when applied to datasets with large numbers of individuals and items, the standard EM algorithm can be slow to converge, with computationally expensive E-steps. We propose a modified EM…
Jie You, Zhaoxuan Li, Junli Du, Praveen Kumar Donta
Gaussian mixture model (GMM) is a very useful tool, which is widely used in complex probability distribution modeling, such as data classification , image classification and segmentation , speech recognition , etc. The Gaussian mixture model is composed of K single Gaussian distributions. For a single Gaussian…
Dawei Zou, Chunhua Ma, Peng Wang, Yanqiu Geng + 1 more
Hyperparameter optimization (HPO), which is also called hyperparameter tuning, is a vital component of developing machine learning models. These parameters, which regulate the behavior of the machine learning algorithm and cannot be directly learned from the given training data, can significantly affect the performance…
Joan Saurina-i-Ricos, Daniel Mas Montserrat, Alexander G. Ioannidis
Estimating genetic clusters from sequencing data is a fundamental task in population and medical genetics, enabling demographic inference and adjustment for population structure in association studies. ADMIXTURE, a widely used model-based clustering method, employs an accelerated Expectation–Maximization (EM) algorithm…
Weisan Wu
In this paper, we give a modified gradient EM algorithm; it can protect the privacy of sensitive data by adding discrete Gaussian mechanism noise. Specifically, it makes the high-dimensional data easier to process mainly by scaling, truncating, noise multiplication, and smoothing steps on the data. Since the variance…
Zihao Chen, Changhu Wang, Siyuan Huang, Yang Shi + 1 more
In single-cell RNA sequencing (scRNA-seq) studies, cell-types and their associated marker genes are often identified by clustering and differential expression gene (DEG) analysis. scRNA-seq data contain many genes not relevant to cell-types and gene selection procedures are needed for more accurate clustering. An ideal…
Tien-Wen Lee
The General Linear Model (GLM) has been widely used in research, where error term has been treated as noise. However, compelling evidence suggests that in biological systems, the target variables may possess their innate variances. A modified GLM was proposed to explicitly model biological variance and non-biological…
Ming-Yan Gong, Bin Lyu, Chee Kiat Seow, Henrik Hesse + 3 more
'Soon Yim Tan' 'Yunjia Wang'] The existing expectation maximization (EM) and space-alternating generalized EM (SAGE) algorithms are only applied to direction of arrival (DOA) estimation in known noise. In this paper, the two algorithms are designed for DOA estimation in unknown uniform noise. Both the deterministic and…
Joshua P. Kulasingham, Jonathan Z. Simon
The Temporal Response Function (TRF) is a linear model of neural activity time-locked to continuous stimuli, including continuous speech. TRFs based on speech envelopes typically have distinct components that have provided remarkable insights into the cortical processing of speech. However, current methods may lead to…
Jonathan Nir, Leon Y. Deouell
Eye movement (EM) detection is a critical step in most eye-tracking (ET) research, typically relying on detectors – specialized algorithms designed to segment raw ET data into discrete oculomotor events. However, variability in detection algorithms and the lack of standardized evaluation frameworks hinder transparency…
Authors not listed
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…
Meng Jia, Troy Sorensen, Dorit Hammerling
We propose a generic, modular framework to optimize the placement of continuous monitoring sensors on oil and gas sites aiming to maximize the methane emissions detection efficiency. Our proposed framework substantially expands the problem scale compared to previous related studies and can be adapted for different…