Paper mill effluent (PME) was used as an organic feedstock for production of biohydrogen via dark fermentation using heat-shock pretreated anaerobic sludge under mesophilic conditions. The influence of substrate concentration (5, 10 and 15 g-COD/L) and the initial pH (5 and 7) on the efficiency of dark hydrogen fermentation from PME were investigated. The highest hydrogen yield of 55.4 mL/g-COD...

Empirical Bayes inference assumes an unknown prior density g(θ) has yielded (unobservable) Θ1,Θ2, . . . ,ΘN , and each Θi produces an independent observation Xi from pi(Xi|Θi). The marginal density fi(Xi) is a convolution of the prior g and pi. The Bayes deconvolution problem is one of recovering g from the data. Although estimation of g—so called g-modeling—is difficult, the results are more e...

The aim of this study was to investigate the effects of two dietary metabolizable protein (MP) concentrations on the performance and health status of Afshari ewes and survival and growth of their lambs during late pregnancy. For 6 weeks prior to lambing, 32 Afshari ewes were randomly assigned to two dietary treatments, containing low (LMP) or high (HMP) MP concentrations. The ewes in LMP (n=16)...

Zellner’s g-prior remains a popular conventional prior for use in Bayesian variable selection, despite several undesirable consistency issues. In this paper, we study mixtures of g-priors as an alternative to default g-priors that resolve many of the problems with the original formulation, while maintaining the computational tractability that has made the g-prior so popular. We present theoreti...

where X is the design matrix, ∼ N (0, σI), and β ∼ N (β0, gσ(XTX)−1). The prior on σ is the Jeffreys prior, π(σ) ∝ 1 σ2 , and usually, β0 is taken to be 0 for simplification purposes. The appeal of the method is that there is only one free parameter g for all linear regression. Furthermore, the simplicity of the g-prior model generally leads to easily obtained analytical results. However, we st...

A Bayesian approach is proposed for coefficient estimation in Tobit quantile regression model. The proposed approach is based on placing a g-prior distribution depends on the quantile level on the regression coefficients. The prior is generalized by introducing a ridge parameter to address important challenges that may arise with censored data, such as multicollinearity and overfitting problems...

Zellner’s g-prior remains a popular conventional prior for use in Bayesian variable selection, despite several undesirable consistency issues. In this paper, we study mixtures of g-priors as an alternative to default g-priors that resolve many of the problems with the original formulation, while maintaining the computational tractability that has made the g prior so popular. We present theoreti...