Fig. 2
From: Using Bayesian inference to estimate plausible muscle forces in musculoskeletal models
Flow Chart for MCMC and Elbow Flexion System: A The starting proposal for each parameter is drawn from a uniform distribution between [− 15,-5]. There are 60 parameters total representing amplitudes of the compact radial basis functions (CRBFs), 10 parameters for every muscle, where A1,1 is the amplitude of the first node of the first muscle, and A6,10 is the amplitude of the tenth node of the sixth muscle. B The proposal is converted from the set of CRBFs into a muscle excitations (Eqs. 1–2), which are given to OpenSim to generate a reference motion. C The posterior log-probability is calculated from the log likelihood (sum of square errors to the reference motion) and the log prior (the sum of muscle excitations (u) cubed). D The current proposal is accepted or rejected based on the change in posterior log probability from the original proposal to the new proposal (initial proposal is always accepted). E If the current iteration is equal to the pre-defined maximum iterations, the MCMC exits, otherwise it generates a new proposal in F by perturbing the current proposal by a value drawn from a normal distribution and continue to loop through the steps within the green box. Further details on the algorithm and acceptance criteria are given in [39, 45]