Gaussian distribution

Part 1 of ebook "Statistics and analysis of scientific data (Second edition)" provides readers with contents including: Chapter 1 - Theory of probability; Chapter 2 - Random variables and their distributions; Chapter 3 - Three fundamental distributions - binomial, Gaussian, and poisson; Chapter 4 - Functions of random variables and error propagation; Chapter 5 - Maximum likelihood and other methods to estimate variables; Chapter 6 - Mean, median, and average values of variables; Chapter 7 - Hypothesis testing and statistics;...

158p daonhiennhien 03-07-2024 4 1   Download

More and more real-world datasets have heavy-tailed distribution, while the calculations for these distributions in multi-dimensional cases are complex. This work shows a method to investigate data of multivariate heavy-tailed distributions.

11p viambani 18-06-2024 2 1   Download

Probability and Computer science - Lecture 8: Central limit theorems. This lecture provides students with content including: quick look at law of large numbers; normal distribution; central limit theorem;... Please refer to the detailed content of the lecture!

13p codabach1016 03-05-2024 7 0   Download

This paper presents a theoretical study that describes the fiber bridging mechanism under tensile stress using a closed-form analytical function. This proposed analytical model allows estimating the stress at a crack opening where the fiber orientation is represented by a Gaussian-like periodic distribution function.

14p viohoyo 25-04-2024 2 2   Download

Value distribution theory of the Gauss map of complete regular minimal surfaces has a long history, in particular, much attention has been given to this theory from the viewpoint of the Nevanlinna theory. In this article, we will establish an estimate for the Gaussian curvature of minimal surfaces in R 4 whose classical Gauss map is ramified over the set of distinct points.

12p visharma 20-10-2023 3 2   Download

The paper "Image processing-based automatic gradation of stone aggregates" aims to employ image processing methods to develop a simple tool for automatic gradation of stone aggregates. The methods of image thresholding, Gaussian filtering, median filtering, morphological closing, and contour analysis are employed. The output of the newly constructed system is the plots demonstrating particle size distribution. These plots can be used for further inspection of aggregate gradation. The system has been developed in Python and with the help of the OpenCV library.

6p nhanchienthien 25-07-2023 6 3   Download

GAW20 working group 5 brought together researchers who contributed 7 papers with the aim of evaluating methods to detect genetic by epigenetic interactions. GAW20 distributed real data from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, including single-nucleotide polymorphism (SNP) markers, methylation (cytosine-phosphate-guanine [CpG]) markers, and phenotype information on up to 995 individuals.

7p vihagrid 30-01-2023 5 3   Download

Previous studies have observed mineral specific variations in the ratios of IRSL signals stimulated at the different discrete wavelengths of lines corresponding to Xe lamp emission lines, which fall within the broad excitation spectrum of IRSL. Meanwhile recent accounts of IRSL have referred to single trapping systems within the excitation band, and modelled excitation spectra as continuous gaussian distributions.

8p vironald 15-12-2022 19 4   Download

Lecture Data mining: Lesson 14. The main topics covered in this chapter include: cluster analysis; model-based clustering methods; model based clustering; assuming d-dim Gaussian distributions; cluster validity; measures of cluster validity;... Please refer to the content of document.

26p tieuvulinhhoa 22-09-2022 7 4   Download

In this paper, we propose a novel method to effectively detect GNSS (Global Navigation Satellite Systems) spoofing signals. Our approach utilizes mixtures of Gaussian distributions to model the Carrier Phase’s Double Difference (DD) produced by two separated receivers.

6p vimelindagates 18-07-2022 4 2   Download

Lecture Artificial Intelligence - Chapter 14a: Bayesian networks. The main contents of this chapter include all of the following: Bayes nets provide a natural representation for (causally induced) conditional independence, topology + CPTs = compact representation of joint distribution, Generally easy for (non)experts to construct, Canonical distributions (e.g., noisy-OR) = compact representation of CPTs, Continuous variables ⇒ parameterized distributions (e.g., linear Gaussian).

29p cucngoainhan0 10-05-2022 11 2   Download

In template-based modeling when using a single template, inter-atomic distances of an unknown protein structure are assumed to be distributed by Gaussian probability density functions, whose center peaks are located at the distances between corresponding atoms in the template structure.

12p vikentucky2711 24-11-2020 12 1   Download

Computer simulation is a resource which can be employed to identify optimal breeding strategies to effectively and efficiently achieve specific goals in developing improved cultivars. In some instances, it is crucial to assess in silico the options as well as the impact of various crossing schemes and breeding approaches on performance for traits of interest such as grain yield.

15p vioklahoma2711 19-11-2020 16 1   Download

Performing statistical tests is an important step in analyzing genome-wide datasets for detecting genomic features differentially expressed between conditions. Each type of statistical test has its own advantages in characterizing certain aspects of differences between population means and often assumes a relatively simple data distribution (e.g., Gaussian, Poisson, negative binomial, etc.), which may not be well met by the datasets of interest.

19p vioklahoma2711 19-11-2020 12 0   Download

Traditional econometric modelling typically follows the idea that market returns follow a normal distribution. However, the concept of tail risk indicates that the distribution of returns is not normal, but skewed and has heavy tails. Thus, a heavy-tailed distribution, which accurately estimates the tail risk, would significantly improve quantitative risk management practice. In this paper, we compare four widely used heavy-tailed distributions using the S&P 500 daily returns.

11p nguyenanhtuan_qb 09-07-2020 14 2   Download

In this paper, we will make explicit the error in the mean value and the standard deviation when using different types of distribution laws. We also employ the principle of maximum entropy as a criterion to choose among the truncated Gaussian, the fitted Gaussian and the lognormal distribution.

8p christabelhuynh 29-05-2020 9 1   Download

In this paper, numerical and experimental approaches to this phenomenon on were conducted. Numerical method comprised of couple heat-displacement. In it, heat flux distribution of laser beam was applied on the steel layer in Gaussian form and by using subroutine code writing procedure.

10p tohitohi 19-05-2020 9 0   Download

The aim of this paper is to empirically investigate the in sample and out of sample forecasting performance of several GARCH-type models such as GARCH, EGARCH and APARCH model with Gaussian, student-t, Generalized error distribution (GED), student-t with fixed DOF 10 and GED with fixed parameter 1.5 distributional assumption in case of Colombo Stock Exchange (CSE), Sri Lanka. The daily All Share Price Index (ASPI) of CSE from January 02, 1998 to December 29, 2006 for a total number of 2150 observations is used for empirical analysis.

14p covid19 19-04-2020 16 4   Download

It is well-known that some famous probability density functions (PDF) of random variables are associated with symmetries of these random variables. The Boltzmann and Gaussian PDFs that are invariant under translation and spherical transformations of their variables, respectively, are obvious and well-studied examples reflecting not only symmetries of many physical phenomena but also their underlying conservation laws.

14p 035522894 26-03-2020 47 2   Download

Value at Risk (VaR) is the most popular market risk measure as it summarizes in one figure the exposure to different risk factors. It had been around for over a decade when Expected Shortfall (ES) emerged to correct its shortcomings. Both risk measures can be estimated under several models. We explore the application of a parametric model to fit the joint distribution of risk factor returns based on multivariate finite Gaussian Mixtures, derive a closed-form expression for ES under this model and estimate risk measures for a multi-asset portfolio over an extended period.

17p cothumenhmong4 24-03-2020 24 2   Download