Related papers: Hubble constant by natural selection: Evolution ch…

Approximate Bayes Computations (ABC) are used for parameter inference when the likelihood function of the model is expensive to evaluate but relatively cheap to sample from. In particle ABC, an ensemble of particles in the product space of…

Approximate Bayesian computation (ABC) or likelihood-free inference algorithms are used to find approximations to posterior distributions without making explicit use of the likelihood function, depending instead on simulation of sample data…

One way that an anthropic selection mechanism may be manifest in a physical theory involves multiple domains in the universe with different values of the physical parameters. If this mechanism is to be relevant for understanding the small…

The determination of the Hubble constant $H_0$ from the Cosmic Microwave Background by the Planck Collaboration [Aghanim et al. 2018] is in tension at $4.2\sigma$ with respect to the local determination of $H_0$ by the SH0ES collaboration…

The fine-tuning of the universe for life, the idea that the constants of nature (or ratios between them) must belong to very small intervals in order for life to exist, has been debated by scientists for several decades. Several criticisms…

The recent local measurement of the Hubble constant based on the distance-ladder method has reached a $1.91\%$ precision, but this result is in tension with the early-universe measurements at the more than 4$\sigma$ level, bringing a crisis…

For 100 years since galaxies were found to be flying apart from each other, astronomers have been trying to determine how fast. The expansion, characterized by the Hubble constant, H0, is confused locally by peculiar velocities caused by…

Approximate Bayesian computation performs approximate inference for models where likelihood computations are expensive or impossible. Instead simulations from the model are performed for various parameter values and accepted if they are…

We present a new solution to the cosmological constant (CC) and coincidence problems in which the observed value of the CC, $\Lambda$, is linked to other observable properties of the universe. This is achieved by promoting the CC from a…

In the following article we consider approximate Bayesian computation (ABC) for certain classes of time series models. In particular, we focus upon scenarios where the likelihoods of the observations and parameter are intractable, by which…

Approximate Bayesian Computation (ABC) is a popular method for approximate inference in generative models with intractable but easy-to-sample likelihood. It constructs an approximate posterior distribution by finding parameters for which…

Approximate Bayesian computation (ABC) methods, which are applicable when the likelihood is difficult or impossible to calculate, are an active topic of current research. Most current ABC algorithms directly approximate the posterior…

To infer the parameters of mechanistic models with intractable likelihoods, techniques such as approximate Bayesian computation (ABC) are increasingly being adopted. One of the main disadvantages of ABC in practical situations, however, is…

An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data. This is a difficult problem, because of the substantial complexity and variety in human behavioral strategies. We report…

Stochastic systems in biology often exhibit substantial variability within and between cells. This variability, as well as having dramatic functional consequences, provides information about the underlying details of the system's behaviour.…

Since the expansion of the universe was first established by Edwin Hubble and Georges Lemaitre about a century ago, the Hubble constant H0 which measures its rate has been of great interest to astronomers. Besides being interesting in its…

Approximate Bayesian Computation (ABC) has gained popularity as a method for conducting inference and forecasting in complex models, most notably those which are intractable in some sense. In this paper we use ABC to produce probabilistic…

Even though the Hubble constant cannot be significantly determined by the low-redshift Baryon Acoustic Oscillation (BAO) data alone, it can be tightly constrained once the high-redshift BAO data are combined. Combining BAO data from 6dFGS,…

In this work, we reconstruct the H(z) based on observational Hubble data with Artificial Neural Network, then estimate the cosmological parameters and the Hubble constant. The training data we used are covariance matrix and mock H(z), which…

The standard cosmological model, the $\Lambda$CDM model, is the most suitable description for our universe. This framework can explain the accelerated expansion phase of the universe but still is not immune to open problems when it comes to…