Applied and Computational Engineering
- The Open Access Proceedings Series for Conferences
Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
Series Vol. 6 , 14 June 2023
Open
Access | Article
Statistical species distribution and their respective development
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Abstract
A distribution is a set of information on a variable. When these data are normally grouped in size order from least to largest, they may then be visually represented. In fact, it is fairly simple to comprehend statistical distributions in terms of functional relationships. Simply put, a data variable is thought of as being coupled with another data variable or several data variables into specific functional relationships, the majority of which can be reflected in the coordinate axes. Once the distribution function has been built, it may be swiftly used to define and compute significant variables, such as an observation's probability, as well as to show the relationships between observations in the domain. The distribution of statistics is very broad, it goes deep into various fields of study, with different specialties and models combined. The process of discovering them is also different, and again, different distributions apply to different scenarios. When faced with different mathematical models, Mathematicians should choose the most suitable distribution method to calculate the probability density function, cumulative distribution function, and calculate the probability and expected value, so as to correctly understand the model. The following article will focus on different definitions of statistical distributions and their origins.
Keywords
probability space, random variables.
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
- ISBN (Print)
- 978-1-915371-59-1
- ISBN (Online)
- 978-1-915371-60-7
- Published Date
- 14 June 2023
- Series
- Applied and Computational Engineering
- ISSN (Print)
- 2755-2721
- ISSN (Online)
- 2755-273X
- DOI
- 10.54254/2755-2721/6/20230813
- Copyright
- 14 June 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited