Related papers: Deriving star formation histories: Inverting HR di…

In exploratory factor analysis, model parameters are usually estimated by maximum likelihood method. The maximum likelihood estimate is obtained by solving a complicated multivariate algebraic equation. Since the solution to the equation is…

Finding the most likely (MAP) configuration of a Markov random field (MRF) is NP-hard in general. A promising, recent technique is to reduce the problem to finding a maximum weight stable set (MWSS) on a derived weighted graph, which if…

This paper investigates the problem of reconstructing a random source from statistical phaseless data for the two-dimensional Helmholtz equation. The major challenge of this problem is non-uniqueness, which we overcome through a reference…

Star Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFRs are usually estimated via spectroscopic observations requiring large amounts of telescope time. We explore an alternative approach based…

We propose a coherent method for the detection and reconstruction of gravitational wave signals for a network of interferometric detectors. The method is derived using the likelihood functional for unknown signal waveforms. In the standard…

We introduce a new method to infer the posterior distribution for planet occurrence rates from radial-velocity (RV) observations. The approach combines posterior samples from the analysis of individual RV datasets of several stars, using…

We adapt an existing data compression algorithm, MOPED, to the extraction of median-likelihood star formation (SF) histories from medium-resolution galaxy spectra. By focusing on the high-pass components of galaxy spectra, we minimize…

From an initial sample of a few hundred stars selected for an Hipparcos program, a subsample of 33 objects with direct determination of their effective temperature, bolometric magnitude and [Fe/H] has been derived. An accurate observational…

The determination of galaxy merger fraction of field galaxies using automatic morphological indices and photometric redshifts is affected by several biases if observational errors are not properly treated. Here, we correct these biases…

We have developed a new regression technique, the maximum likelihood (ML)-based method and its variant, the KS-test based method, designed to obtain unbiased regression results from typical astronomical data. A normalizing flow model is…

Metabolic monitoring and reaction rate estimation using hyperpolarized NMR technology requires accurate quantitative analysis of multidimensional data scenarios. Currently, this analysis is often performed in a two-stage procedure, which is…

We trace the specific star formation rate (sSFR) of massive star-forming galaxies ($\gtrsim\!10^{10}\,\mathcal{M}_\odot$) from $z\sim2$ to 7. Our method is substantially different from previous analyses, as it does not rely on direct…

We present a new method for deriving the stellar birth function (SBF) of resolved stellar populations. The SBF (stars born per unit mass, time, and metallicity) is the combination of the initial mass function (IMF), the star-formation…

Maximum likelihood iteration is one of the most commonly used reconstruction algorithms in quantum tomography. The main appeal of the method is that it is easy to implement and that it converges reliably to a physically meaningful density…

In this paper, we study the problem of inferring time-varying Markov random fields (MRF), where the underlying graphical model is both sparse and changes sparsely over time. Most of the existing methods for the inference of time-varying…

In most star formation history (SFH) measurements, the reported uncertainties are those due to effects whose sizes can be readily measured: Poisson noise, adopted distance and extinction, and binning choices in the solution itself. However,…

This thesis develops a method for obtaining the star formation histories of a mixed, resolved population through the use of color-magnitude diagrams (CMDs). The method is applied to the derivation of the local star formation rate, modeling…

We present an Expectation-Maximization algorithm for the fractal inverse problem: the problem of fitting a fractal model to data. In our setting the fractals are Iterated Function Systems (IFS), with similitudes as the family of…

Upcoming synoptic surveys are set to generate an unprecedented amount of data. This requires an automatic framework that can quickly and efficiently provide classification labels for several new object classification challenges. Using data…

I propose a variational approach to maximum pseudolikelihood inference of the Ising model. The variational algorithm is more computationally efficient, and does a better job predicting out-of-sample correlations than $L_2$ regularized…