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Bayesian Inference of Ammunition Consumption Based on Normal-Inverse Gamma Distribution [PDF]

open access: goldDiscrete Dynamics in Nature and Society, 2022
To address the problems of high cost of new ammunition experiment, few data of field test and low accuracy of consumption prediction, this article proposes a Bayesian estimation method of ammunition consumption based on normal-inverse gamma distribution,
Haobang Liu   +5 more
doaj   +5 more sources

invgamma: the inverse gamma distribution in R [PDF]

open access: goldPeerJ Computer Science
invgamma is a popular low dependency R package that implements the probability density function (PDF), cumulative distribution function (CDF), quantile function (QF) and random number generator (RNG) functions for the inverse gamma, inverse chi-squared ...
David Kahle, James Stamey
doaj   +5 more sources

The Inverse Gamma Distribution and Benford's Law [PDF]

open access: diamondThe PUMP Journal of Undergraduate Research, 2020
According to Benford's Law, many data sets have a bias towards lower leading digits (about 30% are 1's). The applications of Benford's Law vary: from detecting tax, voter and image fraud to determining the possibility of match-fixing in competitive ...
Rebecca F. Durst   +6 more
semanticscholar   +6 more sources

Approximations for the inverse cumulative distribution function of the gamma distribution used in wireless communication [PDF]

open access: goldHeliyon, 2020
The use of quantile functions of probability distributions whose cumulative distribution is intractable is often limited in Monte Carlo simulation, modeling, and random number generation.
Hilary Okagbue   +2 more
doaj   +3 more sources

Weighted Analogue of Inverse Gamma Distribution: Statistical Properties, Estimation and Simulation Study

open access: diamondPakistan Journal of Statistics and Operation Research, 2019
In this article we propose a new weighted version of inverse Gamma distribution known as Weighted Inverse Gamma distribution (WIGD). We examine the Length biased and Area biased versions of Weighted Inverse Gamma distribution. Basic structural properties
Afaq Ahmad, S. P. Ahmad
semanticscholar   +4 more sources

Confidence intervals for the coefficient of variation and the difference between coefficients of variation of inverse-gamma distributions [PDF]

open access: greenSongklanakarin Journal of Science and Technology (SJST), 2022
The aim of this study is to establish new confidence intervals for the single coefficient of variation of an inversegamma distribution using Bayesian methods based on the Jeffreys, reference, and uniform priors and compare them with the Wald method. The
Theerapong Kaewprasert   +2 more
doaj   +3 more sources

Joint distribution properties of fully conditional specification under the normal linear model with normal inverse-gamma priors [PDF]

open access: goldScientific Reports, 2023
Fully conditional specification (FCS) is a convenient and flexible multiple imputation approach. It specifies a sequence of simple regression models instead of a potential complex joint density for missing variables.
Mingyang Cai   +2 more
doaj   +7 more sources

Inverse Generalized Gamma Distribution with it's properties [PDF]

open access: goldالمجلة العراقية للعلوم الاحصائية, 2020
: In this paper, we introduce a new life time distribution . This distribution based on the reciprocal of Generalized Gamma (GG) random variable . This new distribution is called the Inverse Generalized Gamma (IGG) Distribution in which some of the ...
Hayfa Abdul Jawad Saieed   +2 more
doaj   +4 more sources

The Inverse Gamma Distribution: A New Shadowing Model [PDF]

open access: yes2019 8th Asia-Pacific Conference on Antennas and Propagation (APCAP), 2019
In this paper, we provide empirical evidence that the inverse gamma distribution is an excellent alternative for the lognormal and gamma distributions which are often used to model shadowing.
Seong Ki Yoo   +3 more
semanticscholar   +3 more sources

Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions [PDF]

open access: green, 2021
Multimodal regression is a fundamental task, which integrates the information from different sources to improve the performance of follow-up applications. However, existing methods mainly focus on improving the performance and often ignore the confidence of prediction for diverse situations.
Huan Ma   +5 more
  +6 more sources

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