# Bayesian regression and Bitcoin

arXiv: https://arxiv.org/abs/1410.1231 · HTML: https://mlsanity.com/paper/1410.1231/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2014.

## Built on by (most cited first, 6 of 6)

- [Bitcoin Price Predictive Modeling Using Expert Correction](https://mlsanity.com/paper/2201.02729/) (2022)
- [Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints](https://mlsanity.com/paper/2311.03376/) (2023)
- [Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review](https://mlsanity.com/paper/2202.10098/) (2022)
- [Analytics of Business Time Series Using Machine Learning and Bayesian Inference](https://mlsanity.com/paper/2205.12905/) (2022)
- [Cryptocurrency Price Prediction using Twitter Sentiment Analysis](https://mlsanity.com/paper/2303.09397/) (2023)
- [Bitcoin Price Forecasting Based on Hybrid Variational Mode Decomposition and Long Short Term Memory Network](https://mlsanity.com/paper/2510.15900/) (2025)

## Abstract (arXiv)

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.