Comparative Study of Probabilistic and Least-Squares Methods for Developing Predictive Models  

作　　者：Boribo Kikunda Philippe Thierry Nsabimana Jules Raymond Kala Jeremie Ndikumagenge Longin Ndayisaba Boribo Kikunda Philippe;Thierry Nsabimana;Jules Raymond Kala;Jeremie Ndikumagenge;Longin Ndayisaba(Centre de Recherche en Infrastructure Environnement et Technologie (CRIET), Faculty of Engineering Sciences, Universit du Burundi, Bujumbura, Burundi;Faculty of Sciences, Universit Catholique de Bukavu (UCB), Bukavu, DR Congo;Management Computer Department, Institut suprieur Pdagogique de bukavu (ISP/Bukavu), Bukavu, DR Congo)

机构地区：[1]Centre de Recherche en Infrastructure Environnement et Technologie (CRIET), Faculty of Engineering Sciences, Universit du Burundi, Bujumbura, Burundi [2]Faculty of Sciences, Universit Catholique de Bukavu (UCB), Bukavu, DR Congo [3]Management Computer Department, Institut suprieur Pdagogique de bukavu (ISP/Bukavu), Bukavu, DR Congo

出　　处：《Open Journal of Applied Sciences》2024年第7期1775-1787,共13页应用科学（英文）

摘　　要：This article explores the comparison between the probability method and the least squares method in the design of linear predictive models. It points out that these two approaches have distinct theoretical foundations and can lead to varied or similar results in terms of precision and performance under certain assumptions. The article underlines the importance of comparing these two approaches to choose the one best suited to the context, available data and modeling objectives.This article explores the comparison between the probability method and the least squares method in the design of linear predictive models. It points out that these two approaches have distinct theoretical foundations and can lead to varied or similar results in terms of precision and performance under certain assumptions. The article underlines the importance of comparing these two approaches to choose the one best suited to the context, available data and modeling objectives.

关 键 词：Predictive Models Least Squares Bayesian Estimation Methods 

分 类 号：H31[语言文字—英语]