학술논문
Study of measure theory and examples in probability space
Document Type
Dissertation/ Thesis
Author
Source
Subject
Language
English
Abstract
이 논문에서는 측도 이론을 탐구하고 확률 공간의 맥락에서 예시를 제시하였다.첫 장에서는 확률 공간의 정의와 필수적인 개념들을 자세히 살펴보며, 이를 명확히 이해할 수 있도록 예시를 소개하였다. 그 후에는 확률 공간에서의 분포에 중점을 두고대표적인 예시를 살펴보았다. 세 번째 장에서 확률 변수와 관련된 정의와 정리를 기술하였다. 마지막으로, 앞에서 소개한 개념을 활용하여 예시를 풀고 최대한 이해하기쉬운 풀이로 예시를 증명하였다.
In this thesis, we explore measure theory and provide examples in the context of probabilityspaces. Initially, we delve into the definition of probability spaces and essentialconcepts, accompanied by illustrative examples to enhance understanding. Subsequently,we focus on distributions associated with probability spaces, examining representativeexamples. Moving on to the third section, we investigate the definition and theoremsrelated to random variables. Finally, we apply the concepts learned earlier to solve examples,demonstrating straightforward proofs for better comprehension.
In this thesis, we explore measure theory and provide examples in the context of probabilityspaces. Initially, we delve into the definition of probability spaces and essentialconcepts, accompanied by illustrative examples to enhance understanding. Subsequently,we focus on distributions associated with probability spaces, examining representativeexamples. Moving on to the third section, we investigate the definition and theoremsrelated to random variables. Finally, we apply the concepts learned earlier to solve examples,demonstrating straightforward proofs for better comprehension.