  The paper studies the linear model for Bitcoin price which includes
regression features based on Bitcoin currency statistics, mining processes,
Google search trends, Wikipedia pages visits. The pattern of deviation of
regression model prediction from real prices is simpler comparing to price time
series. It is assumed that this pattern can be predicted by an experienced
expert. In such a way, using the combination of the regression model and expert
correction, one can receive better results than with either regression model or
expert opinion only. It is shown that Bayesian approach makes it possible to
utilize the probabilistic approach using distributions with fat tails and take
into account the outliers in Bitcoin price time series.
