> ML_LITERATURE // BAYES-1763-DOCTRINE-OF-CHANCES_v1.0
An Essay towards solving a Problem in the Doctrine of Chances
Thomas Bayes, Richard Price · Philosophical Transactions of the Royal Society of London (1763)
foundational1763foundationalthirdPartyReproduced
Principal Contribution
Formulated inverse probability, enabling belief updating in light of observed evidence (Bayes Theorem).
Operational Relevance
The mathematical foundation of all Bayesian machine learning, MCMC inference, Kalman filters, and probabilistic classification.
Assumptions
- Prior probabilities exist and can be specified prior to data collection
Limitations
- Choice of subjective prior can distort posterior inference in small sample regimes
Connected Algorithms, Architectures & Tools
Related Algorithms:
Related Architectures:
Implementing Libraries: