Bayesian Statistics

Understanding Bayesian Statistics in Data Science

What are Bayesian Statistics?

Bayesian statistics are a mathematical approach for calculating the probability of an event occurring based on relevant prior knowledge and observed evidence. It is named after Thomas Bayes, an 18th-century British mathematician, and is rooted in the concept of Bayesian inference, which is used to update the probability for a hypothesis as more evidence or information becomes available.​

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Example of Projects or Domain

Bayesian statistics has been widely used in various domains including healthcare, finance, marketing, and natural language processing. In healthcare, it has been used for clinical trials and drug effectiveness studies. In finance, Bayesian methods have been applied for risk assessment and portfolio management. Additionally, in natural language processing, Bayesian models have been used for text classification and sentiment analysis.​

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References

1. "Bayesian Statistics" by Prof. A. H. Zuur, Prof. E. N. Ieno, and Prof. C. S. Elphick

2. "Bayesian Data Analysis" by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin

3. "Probabilistic Programming & Bayesian Methods for Hackers" by Cam Davidson-Pilon

4. "Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference" by Cameron Davidson-Pilon

5. "Bayesian Statistics and Marketing" by Rossi, Peter E. and Allenby, Greg M.