Comparar métodos

	Equações Diferenciais Estocásticas (EDEs)	Inferência Bayesiana
Área	Simulação	Estatística
Família	Process / pipeline	Bayesian methods
Ano de origem	1944 (theory); 1992 (numerical framework)	1763
Autor original	Kiyosi Itô (Itô calculus, 1944); Peter Kloeden & Eckhard Platen (numerical methods, 1992)	Thomas Bayes; Pierre-Simon Laplace
Tipo	Continuous-time stochastic process model	Probabilistic inference paradigm
Fonte seminal	Øksendal, B. (2003). Stochastic Differential Equations: An Introduction with Applications (6th ed.). Springer.	Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370–418.
Outros nomes	SDE, Itô equations, Stokastik Diferansiyel Denklemler (SDE)	Bayes inference, Bayesian statistics, Bayesian updating, posterior inference
Relacionados	4	3
Resumo	Stochastic differential equations (SDEs) are differential equation models that combine a deterministic drift term — governing the average tendency of a system — with a stochastic diffusion term driven by a Wiener process (Brownian motion). Pioneered through Itô calculus by Kiyosi Itô in 1944 and given a comprehensive numerical treatment by Kloeden and Platen in 1992, SDEs are the standard modelling language for continuous-time systems subject to random noise, including financial asset prices, population dynamics, and physical processes.	Bayesian inference is a statistical paradigm in which probability represents degrees of belief rather than long-run frequencies. It encodes prior knowledge about parameters in a prior distribution, combines that prior with the likelihood of observed data via Bayes' theorem, and produces a posterior distribution that quantifies updated uncertainty. The foundational theorem was published posthumously by Thomas Bayes in 1763 and subsequently systematized by Pierre-Simon Laplace in his 1812 Théorie analytique des probabilités.