Traditionally, deterministic methods have been applied in digital human modeling (DHM). Transforming the deterministic approach of digital human modeling into a probabilistic approach is natural since there is inherent uncertainty and variability associated with DHM problems. Typically, deterministic studies in this field ignore this uncertainty or try to limit the uncertainty by employing optimization procedures. Due to the variability in the inputs, a deterministic study may not be enough to account for the uncertainty in the system. Probabilistic design techniques allow the designer to predict the likelihood of an outcome while also accounting for uncertainty, in contrast to deterministic studies. The purpose of this study is to incorporate probabilistic approaches to a deterministic DHM problem that has already been studied, analyzing human kinematics and dynamics. The problem is transformed into a probabilistic approach where the human kinematic and dynamic reliabilities are determined. The kinematic reliability refers to the probability that the human end-effector position (and/or orientation) falls within a specified distance from the desired position (and/or orientation) in an inverse kinematic problem. The dynamic reliability refers to the probability that the human end-effector position (and/or velocity) falls within a specified distance from the desired position (and/or velocity) along a specified trajectory in the workspace. The dynamic equations of motion for DHM are derived by the Lagrangian backward recursive dynamics formulation.