Modern logic optimization tools tend to optimize circuits in a balanced way so that all primary outputs (POs) have similar delay close to the cycle time. However, certain POs will be exercised more frequently than the rest. Among these critical primary outputs, some may be stabilized very quickly by input vectors, even if their topological, delays from primary inputs are very long. Knowing the dynamic behavior of a circuit can help optimize the most commonly activated paths and. help engineers understand, how resilient a PO is against dynamic environmental variations such, as voltage fluctuations. In this paper, we describe a tool to analyze the dynamic behavior of a circuit utilizing probabilistic information. The techniques exploit the use of timed, ternaiy decision diagrams (tTDD) to encode stabilization conditions for POs. To compute probabilities based on a tTDD, we propose false assignment pruning and random variable compaction to preserve probability calculation accuracy. To deal with the scalability issue, this paper proposes a new circuit partitioning heuristic to reduce the inaccuracy introduced by partitioning. Compared to the timed simulation results, our tool has a mean absolute error of 2.5% and a root mean square error of 5.3% on average for ISCAS-85 benchmarks. Compared to a state-of-the-art dynamic behavior analysis tool, our tool is on average 40x. faster and can handle circuits that the previous tool cannot.