Laplace's 1812 treatise was the first comprehensive treatment of probability theory. He unified the work of Pascal, Fermat, Bernoulli, and Bayes into a single coherent framework, developing generating functions, the method of characteristic functions, and the central limit theorem in its modern form.
Laplace derived the formula for predicting the probability of a future event based on past observations: if an event has occurred s times in n trials, the probability of it occurring next is (s + 1) / (n + 2). This simple rule captures Bayesian reasoning with a uniform prior and remains a useful baseline estimator.
Laplace famously argued that probability is a measure of ignorance, not of objective randomness. For a sufficiently powerful intellect (Laplace's demon), the universe is deterministic. Probability theory is the tool we use because we are not omniscient — it quantifies what we do not know.
Laplace's framing of probability as the calculus of uncertainty is our operating philosophy. We do not predict the future — we quantify our uncertainty about it, update as evidence arrives, and allocate capital where the probabilities favour us.