20 citations · 71 across the 15 of their papers we have counts for
16 papers
Radiological mapping and uncertainty quantification by a fast Microcanonical Langevin Monte Carlo sampler
Lei Pan, Jaewon Lee, Brian J. Quiter +3
Radiological mapping plays a critical role in nuclear emergency response and environmental management activities. A radiation image, representing the spatial and intensity distribu…
Exoplanet transit search at the detection limit: detection and false alarm vetting pipeline
Jakob Robnik, Uroš Seljak, Jon M. Jenkins +1
One of the primary mission goals of the Kepler space telescope was to detect Earth-like terrestrial planets in the habitable zone around Sun-like stars. These planets are at the de…
Can Microcanonical Langevin Dynamics Leverage Mini-Batch Gradient Noise?
Emanuel Sommer, Kangning Diao, Jakob Robnik +2
Scaling inference methods such as Markov chain Monte Carlo to high-dimensional models remains a central challenge in Bayesian deep learning. A promising recent proposal, microcanon…
Faster parallel MCMC: Metropolis adjustment is best served warm
Jakob Robnik, Uroš Seljak
Despite the enormous success of Hamiltonian Monte Carlo and related Markov Chain Monte Carlo (MCMC) methods, sampling often still represents the computational bottleneck in scienti…
Reassessment of Kepler's habitable zone Earth-like exoplanets with data-driven null-signal templates
Jakob Robnik, Uroš Seljak
One of the primary mission goals of the Kepler space telescope is to detect Earth-like terrestrial planets in the habitable zone around Sun-like stars. Unfortunately, such planets…
Metropolis Adjusted Microcanonical Hamiltonian Monte Carlo
Jakob Robnik, Reuben Cohn-Gordon, Uroš Seljak
Sampling from high dimensional distributions is a computational bottleneck in many scientific applications. Hamiltonian Monte Carlo (HMC), and in particular the No-U-Turn Sampler (…
Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks
Emanuel Sommer, Jakob Robnik, Giorgi Nozadze +2
Despite recent advances, sampling-based inference for Bayesian Neural Networks (BNNs) remains a significant challenge in probabilistic deep learning. While sampling-based approache…
Practical and Scalable Hamiltonian Monte Carlo Without the Metropolis Test
Jakob Robnik, Reuben Cohn-Gordon, Uroš Seljak
Hamiltonian Monte Carlo and underdamped Langevin Monte Carlo are leading methods for sampling from high-dimensional distributions with differentiable densities. Both rely on numeri…
Periodicity significance testing with null-signal templates: reassessment of PTF's SMBH binary candidates
Jakob Robnik, Adrian E. Bayer, Maria Charisi +3
Periodograms are widely employed for identifying periodicity in time series data, yet they often struggle to accurately quantify the statistical significance of detected periodic s…
Fluctuation without dissipation: Microcanonical Langevin Monte Carlo
Jakob Robnik, Uroš Seljak
Stochastic sampling algorithms such as Langevin Monte Carlo are inspired by physical systems in a heat bath. Their equilibrium distribution is the canonical ensemble given by a pre…
Microcanonical Hamiltonian Monte Carlo
Jakob Robnik, G. Bruno De Luca, Eva Silverstein +1
We develop Microcanonical Hamiltonian Monte Carlo (MCHMC), a class of models which follow a fixed energy Hamiltonian dynamics, in contrast to Hamiltonian Monte Carlo (HMC), which f…
Statistical significance testing for mixed priors: a combined Bayesian and frequentist analysis
Jakob Robnik, Uroš Seljak
In many hypothesis testing applications, we have mixed priors, with well-motivated informative priors for some parameters but not for others. The Bayesian methodology uses the Baye…
Self-Calibrating the Look-Elsewhere Effect: Fast Evaluation of the Statistical Significance Using Peak Heights
Adrian E. Bayer, Uros Seljak, Jakob Robnik
In experiments where one searches a large parameter space for an anomaly, one often finds many spurious noise-induced peaks in the likelihood. This is known as the look-elsewhere e…
Kepler-90: Giant transit-timing variations reveal a super-puff
Yan Liang, Jakob Robnik, Uros Seljak
Exoplanet Transit Timing Variations (TTVs) caused by gravitational forces between planets can be used to determine planetary masses and orbital parameters. Most of the observed TTV…
Matched filtering with non-Gaussian noise for planet transit detections
Jakob Robnik, Uroš Seljak
We develop a method for planet detection in transit data, which is based on the Matched Filter technique, combined with the Gaussianization of the noise outliers. The method is bas…
Kepler data analysis: non-Gaussian noise and Fourier Gaussian process analysis of star variability
Jakob Robnik, Uroš Seljak
We develop a statistical analysis model of Kepler star flux data in the presence of planet transits, non-Gaussian noise, and star variability. We first develop a model for Kepler n…