We propose a novel approach that leverages Bayesian program analysis to guide large-scale target-guided greybox fuzzing (LTGF). LTGF prioritizes program locations (targets) that are likely to contain bugs and applies directed mutation towards high-priority targets. However, existing LTGF approaches suffer from coarse and heuristic target prioritization strategies, and lack a systematic design to fully exploit feedback from the fuzzing process. We systematically define this prioritization process as the reachable fuzzing targets problem. Bayesian program analysis attaches probabilities to analysis rules and transforms the analysis results into a Bayesian model. By redefining the semantics of Bayesian program analysis, we enable the prediction of whether each target is reachable by the fuzzer, and dynamically adjust the predictions based on fuzzer feedback. On the one hand, Bayesian program analysis builds Bayesian models based on program semantics, enabling systematic and fine-grained prioritization. On the other hand, Bayesian program analysis systematically learns feedback from the fuzzing process, making its guidance adaptive. Moreover, this combination extends the application of Bayesian program analysis from alarm ranking to fully automated bug discovery. We implement our approach and evaluate it against several state-of-the-art fuzzers. On a suite of real-world programs, our approach discovers $3.25 \times$ to $13 \times$ more unique bugs compared to baselines. In addition, our approach identifies 39 previously unknown bugs in well-tested programs, 30 of which have been assigned CVEs.