“…74 For example, in geotechnical engineering, there is a steadily growing body of literature on the application of Bayesian analysis to inverse modeling and quantification of model parameter and output uncertainty. 51,[55][56][57][58]60,70 Bayesian inference allows for an exact description of parameter uncertainty by treating the parameters (and nuisance variables) as probabilistic variables with joint posterior probability density function, 𝑝(θ|σ 1 ). This multivariate distribution, the so-called posterior parameter distribution, is the consequence of two antecedents, a prior distribution, 𝑝(θ), which captures our initial degree of beliefs in the values of the model parameters, θ, and a likelihood function, 𝐿(θ|σ 1 ), which quantifies, by the rules of probability theory, the level of confidence (= conditional belief) in the parameter values in light of the observed peak strength data, σ1 = (σ 1,1 , σ1,2 , … , σ1,𝑛 ), where the subscripts of σ1,2 refer to sample 2 of the peak strength σ1 , etc.…”