24 papers · ranked by Valyu relevance
Raghu Kacker, Ingram Olkin
This article is a survey of the tables of probability distributions published about or after the publication in 1964 of the Handbook of Mathematical Functions, edited by Abramowitz and Stegun
M. M. Hammad
Statistics provides us with the means to make sense of data, uncover patterns, and draw meaningful conclusions from seemingly complex information. It has applications in a wide range of disciplines, including science, engineering, finance, social sciences, and many others. Meanwhile, Mathematica, a powerful…
Henk van Elst
These lecture notes were written with the aim to provide an accessible though technically solid introduction to the logic of systematical analyses of statistical data to undergraduate and to postgraduate students, in particular in the Social Sciences and in Economics. They may also serve as a general reference for the…
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…
Lakshman Mahto
Example 2.41 (Oral exam). In an oral exam you have to solve exactly one problem, which might be one of three types, A, B, or C, which will come up with probabilities 30%, 20%, and 50%, respectively. During your preparation you have solved 9 of 10 problems of type A, 2 of 10 problems of type B, and 6 of 10 problems of…
H. Fakoor, J. Alizadeh Kaklar
Risk evaluation for fatigue failure of the engineering components is an important aspect of the engineering design. Weibull distributions are often used in preference to the log-normal distribution to analyze probability aspects of fatigue results. This study presents a probabilistic model for calculating Weibull…
Oscar Sheynin
0.2. The object of the theory of probability Chapter 1. Main notions, theorems and formulas 1.1. Probability 1.1.1. Theoretical probability 1.1.2. Geometric probability 1.1.3. Statistical probability 1.1.4. Independence of events and observations 1.2. Randomness and random variables 1.2.1. Randomness 1.2.2. Cause or…
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…
Warisa Thangjai, Sa-Aat Niwitpong, Suparat Niwitpong, Maria Gavrilescu
'Maria Gavrilescu'] The Birnbaum-Saunders distribution plays a crucial role in statistical analysis, serving as a model for failure time distribution in engineering and the distribution of particulate matter 2.5 (PM2.5) in environmental sciences. When assessing the health risks linked to PM2.5, it is crucial to give…
Xinjia Chen
| 1 | Introduction | | 3 | | --- | --- | --- | --- | | 2 | Likelihood Ratio Method | | 4 | | | 2.1 | General Principle | 4 | | | 2.2 | Construction of Parameterized Distributions | 5 | | | | 2.2.1 Weight Function | 5 | | | | 2.2.2 Parameter Restriction | 6 | | 3 | | Concentration Inequalities for Univariate…
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…
Authors not listed
Traditional electron-configuration notation (e.g. 1s^2 2s^2 2p^6) compresses multi-electron quantum information into integer occupancies that convey allowed maxima and most-probable arrangements but obscure the underlying probabilistic distribution and the spread of possible measurement outcomes. We present a…
Saralees Nadarajah, Jiahang Lyu, Zheng Xu
Although many data sets are discrete and heavy tailed (for example, number of claims and claim amounts if recorded as rounded values), not many discrete heavy tailed distributions are available in the literature. In this paper, we discuss thirteen known discrete heavy tailed distributions, propose nine new discrete…
Florian H. Hodel, John R. Fieberg
The R package cylcop extends the copula package to allow modeling of correlated circular-linear random variables using copulae that are symmetric in the circular dimension. We present and derive several new circular-linear copulae with this property and demonstrate how they can be implemented in the cylcop package to…
Martin Robinson, Alan Bond, Alexandr Simonov, Jie Zhang + 1 more
Recently, we have introduced the use of techniques drawn from Bayesian statistics to recover kinetic and thermodynamic parameters from voltammetric data, and were able to show that the technique of large amplitude ac voltammetry yielded significantly more accurate parameter values than the equivalent dc approach. In…
Asger Hobolth, Arno Siri-Jégousse, Mogens Bladt
Probability modelling for DNA sequence evolution is well established and provides a rich framework for understanding genetic variation between samples of individuals from one or more populations. We show that both classical and more recent models for coalescence (with or without recombination) can be described in terms…
Lucian Smith, Herbert M. Sauro
In this technical note we describe modifications to Antimony (Biochemical model specification language) that allows modelers to use the SBML distributions package. In addition the article describes best practice for using distributions, including when they should and should not be used.
Asif Ali Wagan, Shahnawaz Talpur, Sanam Narejo, Wei Wang
In various fields, including medical science, datasets characterized by uncertainty are generated. Conventional clustering algorithms, designed for deterministic data, often prove inadequate when applied to uncertain data, posing significant challenges. Recent advancements have introduced clustering algorithms based on…
Jixin Chen
Predicting the reaction kinetics, that is, how fast a reaction can happen in a solution, is essential information for many processes, such as industrial chemical manufacturing, refining, synthesis and separation of petroleum products, environmental processes in air and water, biological reactions in cells, biosensing…
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…
Jixin Chen
Predicting the reaction kinetics, that is, how fast a reaction can happen in a solution, is essential information for many processes, such as industrial chemical manufacturing, refining, synthesis and separation of petroleum products, environmental processes in air and water, biological reactions in cells, biosensing…
Jixin Chen
Predicting the reaction kinetics, that is, how fast a reaction can happen in a solution, is essential information for many processes, such as industrial chemical manufacturing, refining, synthesis and separation of petroleum products, environmental processes in air and water, biological reactions in cells, biosensing…
Jixin Chen
Predicting the reaction kinetics, that is, how fast a reaction can happen in a solution, is essential information for many processes, such as industrial chemical manufacturing, refining, synthesis and separation of petroleum products, environmental processes in air and water, biological reactions in cells, biosensing…
Laura Eslava, Sayle Sigarreta Ricardo, Arno Siri-Jégousse
We prove that the generalized Randić index over graphs following the Erdos-Rényi model, for both the sparse and dense regimes, is concentrated around its mean when the number of vertices tends to infinity.