Graphical abstract for the methodology and contributions in this work. Informative Bayesian Type A uncertainty evaluation and subsequent Monte Carlo propagation to obtain a state of knowledge distribution for the measurand is presented. If independent sampling from the marginal posterior π_μ(m|x₁, … , x_m), cf. (1), is possible, plain Monte Carlo sampling can be used to sample from the posterior distribution for the measurand by combining samples from π_μ(m|x₁, … , x_n) with samples from the Type B state of knowledge PDF π_Z (z). The theoretical justification is given in Section 2 and practical examples are given in Section 3.