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Publication Date

2024-5

First Advisor

Kaitlyn Cook

Document Type

Honors Project

Degree Name

Bachelor of Arts

Department

Mathematics and Statistics

Keywords

Stochastic processes, Bayesian statistics

Abstract

Stochastic processes, one of the most important theories in probability, are the collection of random variables that represent the evolution of their values throughout time. Bayesian statistics is the unified framework for statistical inference that builds on the Bayesian interpretation of probability. Bayesian statistics believe that probability represents our current knowledge about an event of interest. In this paper, we introduce some basic ideas behind stochastic processes and Bayesian statistics and combine these two domains on a CMS hospitalization dataset that concerns the length of hospital stay for patients. Throughout this process, we want to show the usefulness of stochastic processes in Bayesian statistics when we have a non-conjugate prior distribution.

Rights

©2024 Heng Song. Access limited to the Smith College community and other researchers while on campus. Smith College community members also may access from off-campus using a Smith College log-in. Other off-campus researchers may request a copy through Interlibrary Loan for personal use.

Language

English

Comments

xii, 59 pages : color illustrations. Includes bibliographical references.

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