Tootfinder

This https://arxiv.org/abs/2307.00017 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Explicit mutual information for simple networks and neurons with lognormal activities
Networks with stochastic variables described by heavy tailed lognormal distribution are ubiquitous in nature, and hence they deserve an exact information-theoretic characterization. We derive analytical formulas for mutual information between elements of different networks with correlated lognormally distributed activities. In a special case, we find an explicit expression for mutual information between neurons when neural activities and synaptic weights are lognormally distributed, as suggeste…

I keep meeting students who feel they *must* test their variables for normality before analysis.
I tell them there's no need, & if the test tells them it's normal it's only because N is too small.
I decided to run some simulations to check, though, whether e.g., t-tests degraded more for non-normal data & small Ns.
I was a bit surprised by the result: CI coverage degrades for normal vars just as badly as other symmetric dists, but skewed distributions…

A Bayesian multilevel hidden Markov model with Poisson-lognormal emissions for intense longitudinal count data
S. Mildiner Moraga, E. Aarts
https://arxiv.org/abs/2403.12561

A Bayesian multilevel hidden Markov model with Poisson-lognormal emissions for intense longitudinal count data
Hidden Markov models (HMMs) are probabilistic methods in which observations are seen as realizations of a latent Markov process with discrete states that switch over time. Moving beyond standard statistical tests, HMMs offer a statistical environment to optimally exploit the information present in multivariate time series, uncovering the latent dynamics that rule them. Here, we extend the Poisson HMM to the multilevel framework, accommodating variability between individuals with continuously di…

This https://arxiv.org/abs/2310.12720 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

A modified lognormal approximation of the Lyman-$α$ forest: comparison with full hydrodynamic simulations at $2\leq z\leq 2.7$
Observations of the Lyman-$α$ forest in distant quasar spectra with upcoming surveys are expected to provide significantly larger and higher-quality datasets. To interpret these datasets, it is imperative to develop efficient simulations. One such approach is based on the assumption that baryonic densities in the intergalactic medium (IGM) follow a lognormal distribution. We extend our earlier work to assess the robustness of the lognormal model of the Lyman-$α$ forest in recovering the param…

This https://arxiv.org/abs/2310.16464 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Covariance matrices for the Lyman-$α$ forest using the lognormal approximation
We investigate the nature of correlations in the small-scale flux statistics of the Lyman-$α$ (Ly$α$) forest across redshift bins. Understanding these correlations is important for unbiased cosmological and astrophysical parameter inference using the Ly$α$ forest. We focus on the 1-dimensional flux power spectrum (FPS) and mean flux ($\bar F$) simulated using the semi-numerical lognormal model we developed in earlier work. The lognormal model can capture the effects of long wavelength modes …

This https://arxiv.org/abs/2207.07698 has been replaced.
link: https://scholar.google.com/scholar?q=a

Quasi-Monte Carlo and discontinuous Galerkin
In this study, we consider the development of tailored quasi-Monte Carlo (QMC) cubatures for non-conforming discontinuous Galerkin (DG) approximations of elliptic partial differential equations (PDEs) with random coefficients. We consider both the affine and uniform and the lognormal models for the input random field, and investigate the use of QMC cubatures to approximate the expected value of the PDE response subject to input uncertainty. In particular, we prove that the resulting QMC converg…

Modelling Global Fossil CO2 Emissions with a Lognormal Distribution: A Climate Policy Tool
Faustino Prieto, Catalina B. Garc\'ia-Garc\'ia, Rom\'an Salmer\'on G\'omez
https://arxiv.org/abs/2403.00653

Modelling Global Fossil CO2 Emissions with a Lognormal Distribution: A Climate Policy Tool
Carbon dioxide (CO2) emissions have emerged as a critical issue with profound impacts on the environment, human health, and the global economy. The steady increase in atmospheric CO2 levels, largely due to human activities such as burning fossil fuels and deforestation, has become a major contributor to climate change and its associated catastrophic effects. To tackle this pressing challenge, a coordinated global effort is needed, which necessitates a deep understanding of emissions patterns an…

Distinguishing subsampled power laws from other heavy-tailed distributions
Silja Sormunen, Lasse Leskel\"a, Jari Saram\"aki
https://arxiv.org/abs/2404.09614

Distinguishing subsampled power laws from other heavy-tailed distributions
Distinguishing power-law distributions from other heavy-tailed distributions is challenging, and this task is often further complicated by subsampling effects. In this work, we evaluate the performance of two commonly used methods for detecting power-law distributions - the maximum likelihood method of Clauset et al. and the extreme value method of Voitalov et al. - in distinguishing subsampled power laws from two other heavy-tailed distributions, the lognormal and the stretched exponential dis…

Bayesian mass mapping with weak lensing data using KARMMA -- validation with simulations and application to Dark Energy Survey Year 3 data
Supranta S. Boruah, Pier Fiedorowicz, Eduardo Rozo
https://arxiv.org/abs/2403.05484

Bayesian mass mapping with weak lensing data using KARMMA -- validation with simulations and application to Dark Energy Survey Year 3 data
We update the field-level inference code KARMMA to enable tomographic forward-modelling of shear maps. Our code assumes a lognormal prior on the convergence field, and properly accounts for the cross-covariance in the lensing signal across tomographic source bins. We use mock weak lensing data from N-body simulations to validate our mass-mapping forward model by comparing our posterior maps to the input convergence fields. We find that KARMMA produces more accurate reconstructions than traditio…