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Project Overview

Course: LSTAT2130 - Introduction to Bayesian statistics
Submission Date: May 2023
Authors: Lucas Elvira, Mathieu Graf, Victor Dujardin
Guidance: Professor Philippe Lambert, Teaching Assistant Hortense Doms

As an integral component of my coursework in Bayesian statistics, this project provided a platform to delve into the application of Bayesian analysis to model the growth patterns of cancer cells. The initiative was not only a testament to my analytical prowess but also served as a conduit for a deeper comprehension of the intricate biological systems.

Key Objectives

  • Employ Bayesian statistical methods to construct a model that accurately represents cancer cell growth.
  • Extract and interpret complex biological data using advanced probabilistic models.
  • Utilize statistical software to carry out sophisticated data analysis and inference.

Skills Acquired

  • Statistical Analysis: Mastery of Bayesian statistics principles, including hypothesis formulation, prior selection, and posterior analysis.
  • Data Modeling: Acquired the skill to create and validate predictive models for interpreting biological data.
  • Computational Proficiency: Enhanced use of statistical software tools for executing Bayesian inference.
  • Critical Thinking: Developed the ability to construct and assess hypotheses based on statistical evidence.
  • Research & Collaboration: Conducted thorough research and participated in a team to solve complex issues.
  • Communication: Sharpened skills in communicating complex ideas and findings in a clear and succinct manner.

This endeavor was a cornerstone in my academic journey, blending theoretical knowledge with practical applications in biosciences. It stands as a testament to my capability to address data-driven challenges and derive actionable insights in healthcare and research sectors.

Acknowledgments

I am grateful to Professor Philippe Lambert and Teaching Assistant Hortense Doms for their invaluable guidance. My appreciation also extends to my colleagues, Lucas Elvira and Mathieu Graf, for a collaborative and enriching project experience.