We report the characterization of a transiting hot Jupiter WASP-18b at optical wavelengths measured by exoplanet survey satellite (TESS). analyze publicly available data collected TESS in sector 2. Here, we model systematic noise using Gaussian processes (GPs) and fit it to Markov Chain Monte Carlo (MCMC) method.

We describe the double Markov random field, a natural hierarchical model for a Bayesian approach to model-based textured image segmentation. The model is difficult to implement, even using Markov chain Monte Carlo (MCMC) methods, so we describe an approximation that is computationally feasible. This is applied to a satellite image. We emphasise the valuable additional information about uncertai...

Hamiltonian Monte Carlo (HMC) is a powerful Markov chain Monte Carlo (MCMC) method for performing approximate inference in complex probabilistic models of continuous variables. In common with many MCMC methods, however, the standard HMC approach performs poorly in distributions with multiple isolated modes. We present a method for augmenting the Hamiltonian system with an extra continuous tempe...

Mossel and Vigoda (Reports, 30 September 2005, p. 2207) show that nearest neighbor interchange transitions, commonly used in phylogenetic Markov chain Monte Carlo (MCMC) algorithms, perform poorly on mixtures of dissimilar trees. However, the conditions leading to their results are artificial. Standard MCMC convergence diagnostics would detect the problem in real data, and correction of the mod...

Markov chain Monte Carlo MCMC estimation strategies represent a powerful approach to estimation in psychometric models. Popular MCMC samplers and their alignment with Bayesian approaches to modeling are discussed. Key historical and current developments of MCMC are surveyed, emphasizing how MCMC allows the researcher to overcome the limitations of other estimation paradigms, facilitates the est...

Pratola (2016) introduces a novel proposal mechanism for the Metropolis–Hastings step of a Markov chain Monte Carlo (MCMC) sampler that allows efficient traversal of the space of latent stochastic partitions defined by binary regression trees. Here we discuss two considerations: the first is the use of the new proposal mechanism within a population Markov chain Monte Carlo sampler (Geyer, 1991)...

The random numbers driving Markov chain Monte Carlo (MCMC) simulation are usually modeled as independent U(0, 1) random variables. Tribble [24] reports substantial improvements when those random numbers are replaced by carefully balanced inputs from completely uniformly distributed sequences. The previous theoretical justification for using anything other than IID U(0, 1) points shows consisten...

Markov Chain Monte Carlo (MCMC) is a technique for sampling from a target probability distribution, and has risen in importance as faster computing hardware has made possible the exploration of hitherto difficult distributions. Unfortunately, this powerful technique is often misapplied by poor selection of transition kernel for the Markov chain that is generated by the simulation. Some kernels ...

The random numbers driving Markov chain Monte Carlo (MCMC) simulation are usually modeled as independent U(0, 1) random variables. Tribble [24] reports substantial improvements when those random numbers are replaced by carefully balanced inputs from completely uniformly distributed sequences. The previous theoretical justification for using anything other than IID U(0, 1) points shows consisten...

This paper proposes an approach to training rough set models using Bayesian framework trained using Markov Chain Monte Carlo (MCMC) method. The prior probabilities are constructed from the prior knowledge that good rough set models have fewer rules. Markov Chain Monte Carlo sampling is conducted through sampling in the rough set granule space and Metropolis algorithm is used as an acceptance cr...