Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Tak, Hyungsuk"'
Publikováno v:
ApJ, 964, 183 (2024)
The growth of supermassive black holes is strongly linked to their galaxies. It has been shown that the population mean black-hole accretion rate ($\overline{\mathrm{BHAR}}$) primarily correlates with the galaxy stellar mass ($M_\star$) and redshift
Externí odkaz:
http://arxiv.org/abs/2404.00097
Autor:
Vishwanath, Siddharth, Tak, Hyungsuk
We propose a variant of Hamiltonian Monte Carlo (HMC), called the Repelling-Attracting Hamiltonian Monte Carlo (RAHMC), for sampling from multimodal distributions. The key idea that underpins RAHMC is a departure from the conservative dynamics of Ham
Externí odkaz:
http://arxiv.org/abs/2403.04607
Autor:
Tak, Hyungsuk, Ding, Xuheng
We propose a Bayesian meta-analysis to infer the current expansion rate of the Universe, called the Hubble constant ($H_0$), via time delay cosmography. Inputs of the meta-analysis are estimates of two properties for each pair of gravitationally lens
Externí odkaz:
http://arxiv.org/abs/2308.13018
Autor:
Eadie, Gwendolyn M., Speagle, Joshua S., Cisewski-Kehe, Jessi, Foreman-Mackey, Daniel, Huppenkothen, Daniela, Jones, David E., Springford, Aaron, Tak, Hyungsuk
In the last two decades, Bayesian inference has become commonplace in astronomy. At the same time, the choice of algorithms, terminology, notation, and interpretation of Bayesian inference varies from one sub-field of astronomy to the next, which can
Externí odkaz:
http://arxiv.org/abs/2302.04703
Cosmological parameters encoding our understanding of the expansion history of the Universe can be constrained by the accurate estimation of time delays arising in gravitationally lensed systems. We propose TD-CARMA, a Bayesian method to estimate cos
Externí odkaz:
http://arxiv.org/abs/2207.09327
Publikováno v:
The Astronomical Journal, 164 (1), 6, 2022
Most general-purpose classification methods, such as support-vector machine (SVM) and random forest (RF), fail to account for an unusual characteristic of astronomical data: known measurement error uncertainties. In astronomical data, this informatio
Externí odkaz:
http://arxiv.org/abs/2112.06831
Autor:
Hu, Zhirui, Tak, Hyungsuk
In preparation for the era of the time-domain astronomy with upcoming large-scale surveys, we propose a state-space representation of a multivariate damped random walk process as a tool to analyze irregularly-spaced multi-filter light curves with het
Externí odkaz:
http://arxiv.org/abs/2005.08049
Data augmentation (DA) turns seemingly intractable computational problems into simple ones by augmenting latent missing data. In addition to computational simplicity, it is now well-established that DA equipped with a deterministic transformation can
Externí odkaz:
http://arxiv.org/abs/1911.02748
The well-known Bayes theorem assumes that a posterior distribution is a probability distribution. However, the posterior distribution may no longer be a probability distribution if an improper prior distribution (non-probability measure) such as an u
Externí odkaz:
http://arxiv.org/abs/1712.03549
A Gaussian measurement error assumption, i.e., an assumption that the data are observed up to Gaussian noise, can bias any parameter estimation in the presence of outliers. A heavy tailed error assumption based on Student's t distribution helps reduc
Externí odkaz:
http://arxiv.org/abs/1707.03057