Related papers: Bayesian methods for fitting Baryon Acoustic Oscil…
In recent years, the autocorrelation of the hydrogen Lyman-{\alpha} forest has been used to observe the baryon acoustic peak at redshift 2 < z < 3.5 using tens of thousands of QSO spectra from the BOSS survey. However, the interstellar…
Previous studies based on Bayesian methods have shown that the constraints on cosmological parameters from the Baryonic Oscillation Spectroscopic Survey (BOSS) full-shape data using the Effective Field Theory of Large Scale Structure…
In this paper we show how to use data on Lyman-limit and Damped Lyman-alpha absorption systems to derive the hydrogen ionization fractions and the distribution of the face-on total gas column density. We consider axially symmetric, randomly…
We explore the requirements for a Lyman-alpha forest (LyaF) survey designed to measure the angular diameter distance and Hubble parameter at 2~<z~<4 using the standard ruler provided by baryonic acoustic oscillations (BAO). The goal would…
We present predictions for the one-point probability distribution and cumulants of the transmitted QSO flux in the high redshift Lyman-$\alpha$ Forest. We make use of the correlation between the Lyman-$\alpha$ optical depth and the…
We investigate the possibility of detecting the 3D cross correlation power spectrum of the Ly-$\alpha$ forest and HI 21 cm signal from the post reionization epoch. The cross-correlation signal is directly dependent on the dark matter power…
We use 211 galaxy spectra from our survey for Lyman break galaxies (LBGs) associated with 11 damped Lyman alpha systems (DLAs) to measure the three-dimensional LBG auto-correlation and DLA-LBG cross-correlation functions with the primary…
Random forests are a learning algorithm proposed by Breiman [Mach. Learn. 45 (2001) 5--32] that combines several randomized decision trees and aggregates their predictions by averaging. Despite its wide usage and outstanding practical…
Sufficient statistics are combinations of data in terms of which the likelihood function can be rewritten without loss of information. Depending on the data volume reduction, the use of sufficient statistics as a preliminary step in a…
Tree-based ensemble methods such as random forests, gradient-boosted trees, and Bayesianadditive regression trees have been successfully used for regression problems in many applicationsand research studies. In this paper, we study ensemble…
Tree-based priors for probability distributions are usually specified using a predetermined, data-independent collection of candidate recursive partitions of the sample space. To characterize an unknown target density in detail over the…
We compare the observed probability distribution function of the transmission in the \HI\ Lyman-alpha forest, measured from the UVES 'Large Programme' sample at redshifts z=[2,2.5,3], to results from the GIMIC cosmological simulations. Our…
We consider the problems of hypothesis testing and model comparison under a flexible Bayesian linear regression model whose formulation is closely connected with the linear mixed effect model and the parametric models for SNP set analysis…
Bayesian inference is now a leading technique for reconstructing phylogenetic trees from aligned sequence data. In this short note, we formally show that the maximum posterior tree topology provides a statistically consistent estimate of a…
We present a method to recover the shape and amplitude of the power spectrum of mass fluctuations, P(k), from observations of the high redshift \lya forest. The method is motivated by the physical picture of the \lya forest that has emerged…
Random forests are a statistical learning technique that use bootstrap aggregation to average high-variance and low-bias trees. Improvements to random forests, such as applying Lasso regression to the tree predictions, have been proposed in…
Uncovering genuine relationships between a response variable of interest and a large collection of covariates is a fundamental and practically important problem. In the context of Gaussian linear models, both the Bayesian and non-Bayesian…
Determining subgroups that respond especially well (or poorly) to specific interventions (medical or policy) requires new supervised learning methods tailored specifically for causal inference. Bayesian Causal Forest (BCF) is a recent…
Deep learning (DL) has been shown to outperform traditional, human-defined summary statistics of the Ly{\alpha} forest in constraining key astrophysical and cosmological parameters owing to its ability to tap into the realm of non-Gaussian…
Likelihood-free Bayesian inference algorithms are popular methods for calibrating the parameters of complex, stochastic models, required when the likelihood of the observed data is intractable. These algorithms characteristically rely…