Data Science Decoded Data Science #1 - Fisher RA. "On the mathematical foundations of theoretical statistics"(1922)
Jul 7, 2024
Explore the groundbreaking work of Ronald A. Fisher and how it shaped modern statistics. Delve into key concepts like maximum likelihood estimation and its role in parameter estimation. Discover the philosophical clash between frequentist and Bayesian approaches. Learn about the importance of latent representations in machine learning and their connection to Fisher information. The discussion highlights the evolution of statistical methods and the vital link between data compression techniques and statistical estimation.
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AI's Foundation
- Modern AI relies heavily on statistical concepts, not just optimization.
- This makes understanding the history of statistics crucial for AI development.
MLE and AI
- Fisher's work formalized statistical concepts like maximum likelihood estimation (MLE).
- MLE is now central to training AI models by maximizing likelihood.
Frequentist vs. Bayesian
- Frequentists believe a true, deterministic value exists, while Bayesians see probability distributions.
- This core philosophical difference shapes how each approaches statistics.