An approach to the sensitivity analysis of local aposterior inference equations in algebraic Bayesian networks is proposed in the paper. Basic definitions and formulations are briefly given and the development of the matrix-vector approach of a posterior inference is considered. The propagation of deterministic and stochastic evidences in a knowledge pattern with scalar estimates of probabilities of truth over quantum propositions is described. For each of the provided cases necessary metrics are introduced and transformations which result into construction of 4 linear programming problems which solution gives the required estimates are performed. In addition, 2 theorems that postulate the covering estimates for both cases are formulated. The results obtained in the paper prove the correctness of models and create a basis for investigation of local and global logic-probabilistic inference equations sensitivity.