〽ike Dull

Probability theory models data D given parameters θ via p(D|θ). Mathematical statistics inverts this: estimate θ from D. Bayesian inference treats θ as a random variable, updating prior P(θ) into posterior P(θ|D) using Bayes’ theorem, with today’s posterior becoming tomorrow’s prior. In the discrete case, hypotheses H1..Hk get priors P(Hi) and likelihoods P(D|Hi). Posteriors follow from P(Hi|D) ∝ P(D|Hi)P(Hi), with normalization via total probability. A coin example shifts belief from 0.5 to 0.833 after one head. For count data, a Poisson tick-rate model can combine an informative prior (e.g., mean 150) with new observations, using grid approximation, log-likelihoods, and logsumexp for numerical stability. Continuous parameters replace sums with integrals, making evidence hard to compute at scale. Conjugate priors (e.g., beta-binomial) keep updates in closed ... #MQL5 #MT5 #Bayes #AI mql5.com/en/articles/22403?u…