Latent-f and latent-H Gaussian process reconstructions from OHD data both yield f(z), w(z), and Om(z) consistent with Lambda-CDM, with no strong predictive preference and small prior-dependent residuals mainly at high redshift.
Understanding predictive information criteria for Bayesian models
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We review the Akaike, deviance, and Watanabe-Akaike information criteria from a Bayesian perspective, where the goal is to estimate expected out-of-sample-prediction error using a biascorrected adjustment of within-sample error. We focus on the choices involved in setting up these measures, and we compare them in three simple examples, one theoretical and two applied. The contribution of this review is to put all these information criteria into a Bayesian predictive context and to better understand, through small examples, how these methods can apply in practice.
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Latent-Space Gaussian Processes for Dark-Energy Reconstruction from Observational \(H(z)\) Data
Latent-f and latent-H Gaussian process reconstructions from OHD data both yield f(z), w(z), and Om(z) consistent with Lambda-CDM, with no strong predictive preference and small prior-dependent residuals mainly at high redshift.