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Search · four archives
26 papers · ranked by Valyu relevance
Wei-Chia Chen, Juannan Zhou, Jason M Sheltzer, Justin B Kinney + 1 more
Density estimation in sequence space is a fundamental problem in machine learning that is of great importance in computational biology. Due to the discrete nature and large dimensionality of sequence space, how best to estimate such probability distributions from a sample of observed sequences remains unclear. One…
Kang Liu
This paper presents a unified and novel estimation framework for the Weibull, Gamma, and Lognormal distributions based on arbitrary-order moment pairs. Traditional estimation techniques, such as Maximum Likelihood Estimation (MLE) and the classical Method of Moments (MoM), are often restricted to fixed-order moment…
Alex Capaldi, Tiffany N. Kolba, Eugene Demidenko
We propose novel estimators for the parameters of an exponential distribution and a normal distribution when the only known information is a sample of sample maxima; i.e., the known information consists of a sample of m values, each of which is the maximum of a sample of n independent random variables drawn from the…
Hristos Tyralis, Georgia Papacharalampous
Predictions and forecasts of machine learning models should take the form of probability distributions, aiming to increase the quantity of information communicated to end users. Although applications of probabilistic prediction and forecasting with machine learning models in academia and industry are becoming more…
Bhupendra Singh, Varun Agiwal, Ravindra P. Singh, Abhishek Tyagi
In this article, a discrete analogue of continuous Teissier distribution is presented. Its several important distributional characteristics have been derived. The estimation of the unknown parameter has been done using the method of maximum likelihood and the method of moment. Two real data applications have been…
Duarte S. Viana, Luis Santamaría, Jordi Figuerola
Background Propagule retention time is a key factor in determining propagule dispersal distance and the shape of “seed shadows”. Propagules dispersed by animal vectors are either ingested and retained in the gut until defecation or attached externally to the body until detachment. Retention time is a continuous…
Shuji Shinohara, Nobuhito Manome, Kouta Suzuki, Ung-il Chung + 5 more
Bayesian inference is a process of narrowing down hypotheses (causes) to one that best explains observational data (effects). To accurately estimate a cause, a considerable amount of data is required to be observed for as long as possible. However, the object of inference is not always constant. In this case, a method…
Shuji Shinohara, Nobuhito Manome, Kouta Suzuki, Ung-il Chung + 6 more
'Tatsuji Takahashi' 'Hiroshi Okamoto' 'Yukio Pegio Gunji' 'Yoshihiro Nakajima' 'Shunji Mitsuyoshi' 'Enrico Scalas'] Bayesian inference is the process of narrowing down the hypotheses (causes) to the one that best explains the observational data (effects). To accurately estimate a cause, a considerable amount of data is…
C. M. Revathi, Rajesh Moharana
This study proposes the Log-Linear Failure Rate (Log-LFR) distribution, a novel extension of the classical Linear Failure Rate model achieved through a logarithmic transformation. The suggested logarithmic generator is characterized by its survival-based construction, permitting a natural hazard interpretation and an…
Lanxi Zhang, Wenhao Gui, Zihan Zhao, Minghui Liu + 1 more
This study focuses on parameter estimation and reliability analysis for the two-parameter Rayleigh distribution under random censoring. It is shown that directly fitting the standard Rayleigh distribution can lead to substantial estimation errors, especially when the dataset contains a markedly high minimum value. To…
Francisca Javiera Rudolph, José Miguel Ponciano
Understanding ecological variation is fundamental to predicting the dynamics of natural systems, and it becomes increasingly more complex when rare or extreme events are involved. Extreme value statistics are a useful but underutilized tool in ecological analysis, and in this paper we showcase a simulation based study…
Fatma Zehra Doğru, Y. Murat Bulut, Olçay Arslan
The t-distribution has many useful applications in robust statistical analysis. The parameter estimation of the t-distribution is carried out using ML estimation method, and the ML estimates are obtained via the EM algorithm. In this study, we consider an alternative estimation method for all the parameters of the…
Sergio Davis
The probability axioms by R. T. Cox can be regarded as the modern foundations of Bayesian inference, the idea of assigning degrees of belief to logical propositions in a manner consistent with Boolean logic. In this work it is shown that one can start from an alternative point of view, postulating the existence of an…
Kazeem Adesina Dauda, Rasheed Kehinde Lamidi, Adeshola Adediran Dauda, Waheed Babatunde Yahya
In this research, a new class of probability distributions referred to as Generalized Gamma Weibull (GGW) distributions was introduced within the context of parametric survival analysis. This distribution represents a modification of the gamma Weibull distribution and offers valuable insights, particularly when dealing…
Lakshman Mahto
The purpose of the text is to present the computational framework of probability and statistics as a mathematical discipline that observe, understand and applicability of a problem based on probabilistic phenomenon or a mathematical model based on noisy measurements. Natural sciences (e.g. theory of errors in…
Noppadon Yosboonruang, Sa-aat Niwitpong, Suparat Niwitpong, Jianhua Xu
'Jianhua Xu'] Since rainfall data series often contain zero values and thus follow a delta-lognormal distribution, the coefficient of variation is often used to illustrate the dispersion of rainfall in a number of areas and so is an important tool in statistical inference for a rainfall data series. Therefore, the aim…
William Daniels, Meng Jia, Dorit Hammerling
We propose a method for estimating the duration of methane emissions on oil and gas sites, referred to as the Probabilistic Duration Model (PDM), that uses concentration data from continuous monitoring systems (CMS). The PDM probabilistically addresses a key limitation of CMS: non-detect times, or the times when wind…
Wei Ji Ma
A common method, due to 33, for analyzing delayed-estimation data with a circular stimulus variable is to fit a mixture of a Von Mises distribution and a uniform distribution. The uniform distribution represents random guesses, presumably made when an item is not kept in memory. When I generate synthetic data from a…
Kai-Tai Fang, Yu-Xuan Lin, Yu-Hui Deng, Nikolai Leonenko + 1 more
Statistical modeling is fundamentally based on probability distributions, which can be discrete or continuous and univariate or multivariate. This review focuses on the methods used to construct these distributions, covering both traditional and newly developed approaches. We first examine classic distributions such as…
Alexandros Gezerlis, Martin Williams
This article discusses a number of incorrect statements appearing in textbooks on data analysis, machine learning, or computational methods; the common theme in all these cases is the relevance and application of statistics to the study of scientific or engineering data; these mistakes are also quite prevalent in the…
Hao Tang, Tianle Yue, Ying Li
Machine learning (ML) has become an important technique in materials science, markedly accelerating the discovery and design of novel materials, and concurrently lowering the burden of experimental costs. Uncertainty quantification (UQ) plays a pivotal role in the accurate prediction and innovative design of novel…
Maria H. Rasmussen, Chenru Duan, Heather J. Kulik, Jan Halborg Jensen
With the increasingly more important role of machine learning (ML) models in chemical research, the need for putting a level of confidence to the model predictions naturally arises. Several methods for obtaining uncertainty estimates have been proposed in recent years but consensus on the evaluation of these have yet…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…
Authors not listed
Bonkowski and De Souza [Sol. Stat. Ionics 429, 116967 (2025)] provide a guide for performing molecular dynamics simulations of ion transport, including methods for estimating diffusion coefficients and their uncertainties from mean-squared displacement (MSD) data. The discussion of uncertainty in estimated diffusion…
Robert Reischke
Confidence contours in parameter space are a helpful tool to compare and classify determined estimators. For more intricate parameter estimations of non-linear nature or complex error structures, the procedure of determining confidence contours is a statistically complex task. For polymer chemists, such particular…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…