  In this paper, we discuss the method of Bayesian regression and its efficacy
for predicting price variation of Bitcoin, a recently popularized virtual,
cryptographic currency. Bayesian regression refers to utilizing empirical data
as proxy to perform Bayesian inference. We utilize Bayesian regression for the
so-called "latent source model". The Bayesian regression for "latent source
model" was introduced and discussed by Chen, Nikolov and Shah (2013) and
Bresler, Chen and Shah (2014) for the purpose of binary classification. They
established theoretical as well as empirical efficacy of the method for the
setting of binary classification.
  In this paper, instead we utilize it for predicting real-valued quantity, the
price of Bitcoin. Based on this price prediction method, we devise a simple
strategy for trading Bitcoin. The strategy is able to nearly double the
investment in less than 60 day period when run against real data trace.
