Related papers: Hubble constant by natural selection: Evolution ch…
A new approach to inference in state space models is proposed, based on approximate Bayesian computation (ABC). ABC avoids evaluation of the likelihood function by matching observed summary statistics with statistics computed from data…
We study how the determination of the Hubble constant from cosmological distance measures is affected by models of dark energy and vice versa. For this purpose, constraints on the Hubble constant and dark energy are investigated using the…
Approximate Bayesian computation (ABC) refers to a family of inference methods used in the Bayesian analysis of complex models where evaluation of the likelihood is difficult. Conventional ABC methods often suffer from the curse of…
Approximate Bayesian computation (ABC) methods are standard tools for inferring parameters of complex models when the likelihood function is analytically intractable. A popular approach to improving the poor acceptance rate of the basic…
Many scientifically well-motivated statistical models in natural, engineering and environmental sciences are specified through a generative process, but in some cases it may not be possible to write down a likelihood for these models…
Model selection aims to determine which theoretical models are most plausible given some data, without necessarily asking about the preferred values of the model parameters. A common model selection question is to ask when new data require…
We present a determination of the Hubble constant from measurements of the Sunyaev-Zeldovich Effect (SZE) in an orientation-unbiased sample of 7 z < 0.1 galaxy clusters. With improved X-ray models and a more accurate 32-GHz calibration, we…
We use updated Hubble parameter and baryon acoustic oscillation data, as well as other lower-redshift Type Ia supernova, Mg II reverberation-measured quasar, quasar angular size, H II starburst galaxy, and Amati-correlated gamma-ray burst…
Recently much controversy has been raised about the cosmological conundrum involving the discrepancy in the value of the Hubble constant as implied by Planck satellite observations of the CMBR in the early Universe and that deduced from…
Approximate Bayesian computation (ABC) methods perform inference on model-specific parameters of mechanistically motivated parametric statistical models when evaluating likelihoods is difficult. Central to the success of ABC methods is…
Approximate Bayesian computation (ABC) is a method for Bayesian inference when the likelihood is unavailable but simulating from the model is possible. However, many ABC algorithms require a large number of simulations, which can be costly.…
We consider the problem of uniformity testing of Lipschitz continuous distributions with bounded support. The alternative hypothesis is a composite set of Lipschitz continuous distributions that are at least $\varepsilon$ away in $\ell_1$…
We apply a tension metric $Q_\textrm{UDM}$, the update difference in mean parameters, to understand the source of the difference in the measured Hubble constant $H_0$ inferred with cosmic microwave background lensing measurements from the…
Estimates of the Hubble constant, $H_0$, from the distance ladder and the cosmic microwave background (CMB) differ at the $\sim$3-$\sigma$ level, indicating a potential issue with the standard $\Lambda$CDM cosmology. Interpreting this…
We present a simultaneous analysis of 18 galaxy lenses with time delay measurements. For each lens we derive mass maps using pixelated simultaneous modeling with shared Hubble constant. We estimate the Hubble constant to be 66_{-4}^{+6}…
Performing exact posterior inference in complex generative models is often difficult or impossible due to an expensive to evaluate or intractable likelihood function. Approximate Bayesian computation (ABC) is an inference framework that…
We generalise the procedure for joint estimation of cosmological parameters to allow freedom in the relative weights of various probes. This is done by including in the joint Likelihood function a set of 'Hyper-Parameters', which are dealt…
We use three different data sets, specifically $H(z)$ measurements from cosmic chronometers, the HII-galaxy Hubble diagram, and reconstructed quasar-core angular-size measurements, to perform a joint analysis of three flat cosmological…
Approximate Bayesian Computation (ABC) is typically used when the likelihood is either unavailable or intractable but where data can be simulated under different parameter settings using a forward model. Despite the recent interest in ABC,…
We introduce a novel way of measuring $H_0$ from a combination of independent geometrical datasets, with no need of calibration nor of the choice of a cosmological model. We build on the {\it distance duality relation} which sets the ratio…