Code for Bayesian Analysis
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Updated
Apr 13, 2026 - Python
Code for Bayesian Analysis
DerivKit — a robust Python toolkit for stable numerical derivatives
DeepSphere: a graph-based spherical CNN (TensorFlow)
Python code for learning cosmology using different methods and mock data
Flexible, fully bayesian stacking software for modelling of astronomical data sets
CosmicFishPie: Python Fisher Matrix code for Cosmological probes
Correlation functions versus field-level inference in cosmology: example with log-normal fields
Repository containing tutorials about how to use Cobaya for cosmological inference at PhD Schools
Mock CMB likelihood class for Cobaya sampler (https://github.com/CobayaSampler/cobaya), and several specific experiment examples
Collection of Jupyter notebooks demonstrating statistical methods for cosmological data analysis, including Bayesian inference & basic frequentist tools
Main tools and results from arxiv:
Interactive exploration of equivariant neural networks on homogeneous spaces, with a focus on the sphere S² as SO(3)/SO(2). From Lecture 8 of the Lie groups course with Quantum Formalism
JAX-powered Hi-Fi mocks
A Bayesian Python code to confront the quasar data set with models beyond the standard model of elementary particle physics and models beyond the $\Lambda$CDM standard cosmology.
Final release (v1.7) of the Rotational Hemispheric Test project, expanding the v1.1 results and confirming a significant azimuthal anisotropy of FRB dispersion measures around the Siamese CPT-symmetric axis (RA = 170°, Dec = 40°). Includes full data, code, and figures.
The universe may operate as a self-executing algorithm where structure precedes matter. Reality’s “errors”—from cosmic anomalies to quantum correlations—are reflections of its code. Through holography, recursion, and informational self-replication, the cosmos continuously rewrites its own laws.
Testing a CPT-symmetric twin-universe framework where a slight phase desynchronization (≈5%) between Siamese universes generates the observed matter–antimatter asymmetry. Includes numerical scans, CMB–FRB anisotropy tests, and reproducible data analysis scripts.
The codes for computing the scale-dependent peak height function and the scale-dependent valley depth function of the cosmic-log density field.
Flexible, fully bayesian stacking software for modelling of astronomical data sets
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