Static program analysis today takes an analytical approach which is quite suitable for a well-scoped system. Data- and control-flow is taken into account. Special cases such as pointers, procedures, and undefined behavior must be handled. A program is analyzed precisely on the statement level. However, the analytical approach is ill-equiped to handle implementations of complex, large-scale, heterogeneous software systems we see in the real world. Existing static analysis techniques that scale, trade correctness (i.e., soundness or completeness) for scalability and build on strong assumptions (e.g., language-specificity). Scalable static analysis are well-known to report errors that do *not* exist (false positives) or fail to report errors that *do* exist (false negatives). Then, how do we know the degree to which the analysis outcome is correct? In this paper, we propose an approach to scale-oblivious greybox program analysis with bounded error which applies efficient approximation schemes (FPRAS) from the foundations of machine learning: PAC learnability. Given two parameters $δ$ and $ε$, with probability at least $(1-δ)$, our Monte Carlo Program Analysis (MCPA) approach produces an outcome that has an average error at most $ε$. The parameters $δ>0$ and $ε>0$ can be chosen arbitrarily close to zero (0) such that the program analysis outcome is said to be probably-approximately correct (PAC). We demonstrate the pertinent concepts of MCPA using three applications: $(ε,δ)$-approximate quantitative analysis, $(ε,δ)$-approximate software verification, and $(ε,δ)$-approximate patch verification.