An approach to the sensitivity analysis of local a posterior inference equations in algebraic Bayesian networks is proposed in the paper. Performed a sensitivity analysis of first a posterior inference task for stochastic and deterministic evidences propagated into the knowledge pattern with scalar estimates. For each of the considered cases the necessary metrics are chosen and transformations are carried out, that result into a linear programming problem. In addition, for each type of evidence theorems that postulate upper sensitivity estimates are formulated and proofs are provided. Theoretical results are implemented in CSharp using the module of probabilistic-logical inference software complex. A series of computational experiments is conducted. The results of experiments are visualized using tables and charts. The proposed visualization demonstrates the high sensitivity of the considered models, that confirms the correctness of their use.