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- Title
BAYESIAN NEURAL NETWORKS WITH DEPENDENT DIRICHLET PROCESS PRIORS. APPLICATION TO PAIRS TRADING.
- Authors
RUXANDA, Gheorghe; OPINCARIU, Sorin
- Abstract
Bayesian neural networks combine the universality of the neural networks with the principled uncertainty quantification of the Bayesian approach. The black-box character of neural networks makes it difficult establishing appropriate priors for the weights of the neural network. In this paper we propose a hierarchical model where the prior distribution of the network weights is drawn from a Dirichlet process mixture model. We further extend the model to dependent Dirichlet process mixtures to allow the model to account for non-stationarity in the data. The neural network with dependent Dirichlet priors is used to model a pairs trading experiment.
- Subjects
BAYESIAN analysis; DIRICHLET forms; MARKOV chain Monte Carlo; MIXTURE distributions (Probability theory); PAIRS trading
- Publication
Economic Computation & Economic Cybernetics Studies & Research, 2018, Vol 52, Issue 4, p5
- ISSN
0424-267X
- Publication type
Article
- DOI
10.24818/18423264/52.4.18.01