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16.2 Convergence

By definition, a Markov chain generates samples from the target distribution only after it has converged to equilibrium (i.e., equilibrium is defined as being achieved when $p(\theta^{(n)})$ is the target density). The following point cannot be expressed strongly enough:

• In theory, convergence is only guaranteed asymptotically as the number of draws grows without bound.

• In practice, diagnostics must be applied to monitor convergence for the finite number of draws actually available.