Impulse–response functions (IRFs), dynamic-multiplier functions, and forecast-error variance decompositions (FEVDs) are commonly used to describe the results from multivariate time-series models such as VAR models. VAR models have many parameters, which may be difficult to interpret. IRFs and other functions combine the effect of multiple parameters into one summary (per time period). For instance, IRFs measure the effect of a shock (change) in one variable on a given outcome variable.
Bayesian IRFs (and other functions) produce the results using the "exact" posterior distribution of IRFs, which does not rely on the assumption of asymptotic normality. They may also provide more stable estimates for small datasets because they incorporate prior information about the model parameters.