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With the rise of cryptographic ransomware, Bitcoin has found a niche as the standard currency for ransoms. While Bitcoin is pseudonymous, it provides no guarantee of untraceability. As a result, another niche has arisen-Bitcoin money laundering. Hidden Markov Models (HMMs) have previously been used in a number of applications where traditional pattern recognition falls short. In this paper, HMMs are inferred from transactions in the public blockchain in an attempt to link users, events, and enterprises. We introduce a proof-of-concept algorithm to infer HMMs from the Bitcoin blockchain.