چکیده مقاله
An accurate understanding of complicated relations among numerous variables isof significant importance in science One attractive procedure to this task is Gaussian graphicalmodels GGMs , which lately many improvements have been carried out on it GGMs describethe conditional independence among variables by means of the presence or absence of edges inthe related graph In this paper, we recap a Bayesian method for structure learning of GGMsbased on the Birth Death MCMC BDMCMC algorithm We show the application of thismethod on a simulated dataset
کلیدواژهها
نویسندگان
Nastaran Marzban Vaselabadi
Department of Statistics, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr, Iran
Reza Mohammadi
Department of Operation Management, Amsterdam Business School, Amsterdam, Noord Hollan, Netherlands
شیوه ارجاع
Marzban Vaselabadi, Nastaran and Mohammadi, Reza,1403,A REVIEW ON BAYESIAN STRUCTURE LEARNING IN GAUSSIAN GRAPHICAL MODELS,1st International Biennial Conference of Artificial Intelligence and Data Science 2024,Bushehr
ارائهشده در
مجموعه مقالات اولین کنفرانس بین المللی دوسالانه هوش مصنوعی و علوم داده3 اردیبهشت 1403 · بوشهر