Bayesian Networks are graphical representation of dependence relationships between domain variables. They have been applied in many areas due to their powerful probabilistic inference such as data fusion, target recognition, and medical diagnosis, etc. There exists a number of inference algorithms that have different tradeoffs in computational efficiency, accuracy, and applicable network topologies. It is well known that, in general, the exact inference algorithms are either computationally infeasible for dense networks or impossible for mixed discrete-continuous networks. However, in practice, mixed Bayesian Networks are commonly used for various applications. In this paper, we compare and analyze the trade-offs for several inference approaches. They include the exact Junction Tree algorithm for linear Gaussian networks, the exact algorithm for discretized networks, and the stochastic simulation methods. We also propose an almost instant-time algorithm (AIA) by pre-compiling the approximate likelihood tables. Preliminary experimental results show promising performance.