I am not an academic, and it’s been over 15 years since I’ve had any formal math education. My strategy will be to prioritize practical, applied computational methods over complex mathematical proofs in pursuit of an intuitive understanding of modern statistics.
My pursuit is propelled by a fascination with recent AI breakthroughs, a concern for the Replication Crisis in scientific research, and a devotion to open access of knowledge.
Embracing a Work With the Garage Door Open mindset, I am dedicated to sharing the polished outcomes and the learning process alike. If you’re also on a similar path to mastering Bayesian methods or have expertise to share, I’d love to hear from you!
Objectives
My learning objectives are:
- To gain a functional understanding of Bayesian statistics and its application across different fields.
- To become proficient in Julia for statistical modeling and data analysis.
- To contribute to open-source knowledge and tools related to Bayesian methods.
Why Julia?
Though R and Python are prevalent languages in the realm of statistical programming, my choice to utilize Julia is driven by several compelling attributes:
- Performance: Julia is engineered for high performance, with capabilities to efficiently manage the complex computations required for statistical analysis.
- Usability: The language’s elegant syntax and mathematical formulation align with my preferences, offering an intuitive coding experience. Julia’s multiple dispatch paradigm facilitates a natural expression of sophisticated statistical models.
- Reproducibility: Ensuring consistent and verifiable results in statistics is paramount, and Julia’s reproducible environments bolster the reliability of analyses conducted.
- Future-Proofing: Anticipating Julia’s ascent in data science, I am embracing its expanding ecosystem, viewing this venture as an investment in a forward-looking technology.
Committed to this path, I will be translating and adapting reference materials originally in other languages into Julia, showcasing its adaptability and efficacy in statistical practice.
Learning Resources
Primary Resources
The following resources have been selected for their structured approach and practical insights into Bayesian statistics. The sequence builds a strong foundational knowledge before progressing to more complex applications.
Supplemental Resources
These resources dive deeper into the mathematical underpinnings of Bayesian statistics and are recommended for further exploration: