Abstract Decision trees (DT) are highly famous in machine learning and usually acquire state-of-the-art performance. Despite that, well-known variants like CART, ID3, random forest, boosted miss a probabilistic version that encodes prior assumptions about tree structures shares statistical strength between node parameters. Existing work on Bayesian DT depends Markov Chain Monte Carlo (MCMC), wh...

In this paper we propose a Bayesian method for estimating hyperbolic diffusion models. The approach is based on the Markov Chain Monte Carlo (MCMC) method after discretization via the Milstein scheme. Our simulation study shows that the hyperbolic diffusion exhibits many of the stylized facts about asset returns documented in the financial econometrics literature, such as a slowly declining aut...

We have developed a Bayesian model to invert spectral induced-polarization SIP data for Cole-Cole parameters using Markov-chain Monte Carlo MCMC sampling methods. We compared the performance of the MCMC-based stochastic method with an iterative Gauss-Newton-based deterministic method for Cole-Cole parameter estimation through inversion of synthetic and laboratory SIP data. The Gauss-Newton-base...