Comparar métodos

	Modelo de Markov	Programação Dinâmica
Área	Simulação	Otimização
Família	Process / pipeline	Process / pipeline
Ano de origem	1906	1957
Autor original	Andrei Markov	Richard Bellman
Tipo	Probabilistic state-transition model	Exact combinatorial optimization via recursive decomposition
Fonte seminal	Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963	Bellman, R. (1957). Dynamic Programming. Princeton University Press. ISBN: 978-0-691-07951-6
Outros nomes	Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process	DP, Bellman's Principle of Optimality, Recursive Optimization, Dinamik Programlama
Relacionados	5	3
Resumo	A Markov Model represents a system as a finite set of states and specifies the probability of moving from one state to another at each time step. By capturing only the current state — not the full history — it enables tractable analysis of complex dynamic processes across health economics, engineering reliability, operations research, and social-science modeling.	Dynamic Programming (DP) is an exact optimization technique introduced by Richard Bellman in 1957 for solving multi-stage decision problems. It decomposes a complex problem into simpler, overlapping subproblems, solves each subproblem once, and stores the results to avoid redundant computation. Grounded in the Principle of Optimality, DP guarantees globally optimal solutions whenever the problem exhibits overlapping subproblems and optimal substructure.