This paper introduces a Bayesian Markov regime-switching model that allows the cointegration relationship between two time series to be switched on and off over time. Unlike classical approaches for testing and modeling cointegration, the Bayesian Markov switching method allows for estimation of the regime-specific model parameters via Markov Chain Monte Carlo and generates more reliable estima...

In Bayesian estimation the posterior distribution is proportional to likelihood function and the prior density of the parameter. Thus Bayesian inference, most often, is affected by the prior density. In this paper we look at how we can make Bayesian inference more robust against a poorly specified prior. We find that using a mixture of conjugate priors enables us to do this. We allow a small pr...

The contrast sensitivity function that spans the two dimensions of contrast and spatial frequency is crucial in predicting functional vision both in research and clinical applications. In this study, the use of Bayesian inference was proposed to determine the parameters of the two-dimensional contrast sensitivity function. Two-dimensional Bayesian inference was extensively simulated in comparis...

uniaxial compressive strength (ucs) is one of the most significant factors on the stability of underground excavation projects. most of the time, this factor can be obtained by exploratory boreholes evaluation. due to the large distance between exploratory boreholes in the majority of geotechnical projects, the application of geostatistical methods has increased as an estimator of rock mass pro...

The estimation of duration has been shown to follow Bayesian inference, where people use their prior belief to calibrate the estimation. This explains timing biases such as the range bias where a duration is reproduced as longer when previously encountered durations were longer than shorter. However, it is unclear whether prior belief is based on previously perceived or reproduced durations. In...

We consider non-linear Bayesian inversion problems targeting the geostatistical hyperparameters of a random field describing hydrogeological or geophysical properties given data. This problem is particular importance in non-ergodic setting as there are no analytical upscaling relationships linking data to hyperparameters, such as, mean, standard deviation, and integral scales. Full local (typic...