Markov chain Monte Carlo
View article: Bayesian optimization for interval selection in PLS models
Bayesian optimization for interval selection in PLS models Open
View article: Markov Chain Monte Carlo-Guided Compact 3D Gaussian Splatting for Relightable Rendering
Markov Chain Monte Carlo-Guided Compact 3D Gaussian Splatting for Relightable Rendering Open
View article: A Theoretical Bayesian SIR Modeling Framework for Burnout Propagation in Medical Education: Calibrated Simulations, Hierarchical Inference, and Intervention Projections
A Theoretical Bayesian SIR Modeling Framework for Burnout Propagation in Medical Education: Calibrated Simulations, Hierarchical Inference, and Intervention Projections Open
Burnout affects 37.23% (95% CI: 32.66--42.05%) of medical undergraduates globally \cite{almutairi2022prevalence}, rising to 44.2% before residency \cite{frajerman2019burnout} and threatening workforce sustainability. This theoretical manus…
View article: Mici: Manifold Markov chain Monte Carlo methods in Python
Mici: Manifold Markov chain Monte Carlo methods in Python Open
What's Changed 🐛 Bug fixes Fix detupling of VJP in JAX autodiff wrapper by @matt-graham in https://github.com/matt-graham/mici/pull/40 📦 Dependency updates…
View article: A Theoretical Bayesian SIR Modeling Framework for Burnout Propagation in Medical Education: Calibrated Simulations, Hierarchical Inference, and Intervention Projections
A Theoretical Bayesian SIR Modeling Framework for Burnout Propagation in Medical Education: Calibrated Simulations, Hierarchical Inference, and Intervention Projections Open
Burnout affects 37.23% (95% CI: 32.66--42.05%) of medical undergraduates globally \cite{almutairi2022prevalence}, rising to 44.2% before residency \cite{frajerman2019burnout} and threatening workforce sustainability. This theoretical manus…
View article: A Theoretical Bayesian SIR Modeling Framework for Burnout Propagation in Medical Education: Calibrated Simulations, Hierarchical Inference, and Intervention Projections
A Theoretical Bayesian SIR Modeling Framework for Burnout Propagation in Medical Education: Calibrated Simulations, Hierarchical Inference, and Intervention Projections Open
Burnout affects 37.23% (95% CI: 32.66--42.05%) of medical undergraduates globally [Almutairi et al. 2022], rising to 44.2% before residency [Frajerman et al. 2019] and threatening workforce sustainability. This theoretical manuscript prese…
View article: Enhanced Suite of Probabilistic Models for Benchmarking Agentic Debugging Frameworks in Probabilistic Inference
Enhanced Suite of Probabilistic Models for Benchmarking Agentic Debugging Frameworks in Probabilistic Inference Open
This paper presents an extension to the foundational Reactive Oxygen Species (ROS) stochastic differential equation (SDE) model through four probabilistic models implemented in PyMC v5.25.0+. These models provide a robust empirical framewo…
View article: GCV v9.9 - GPU MCMC Analysis: a0 is a Fundamental Constant with Cosmic Origin
GCV v9.9 - GPU MCMC Analysis: a0 is a Fundamental Constant with Cosmic Origin Open
KEY DISCOVERY: a0 is independent of M/L assumptions! Using GPU-accelerated MCMC with FREE mass-to-light ratios: a0 = (1.006 +/- 0.026) x 10^-10 m/s^2 ML_disk = 0.478 (literature: 0.5) ML_bul = 0.721 (literature: 0.7) Cosmic Connection: a0 …
View article: Enhanced Suite of Probabilistic Models for Benchmarking Agentic Debugging Frameworks in Probabilistic Inference
Enhanced Suite of Probabilistic Models for Benchmarking Agentic Debugging Frameworks in Probabilistic Inference Open
This paper presents an extension to the foundational Reactive Oxygen Species (ROS) stochastic differential equation (SDE) model through four probabilistic models implemented in PyMC v5.25.0+. These models provide a robust empirical framewo…
View article: A Novel Multi-Stage Conceptual Protocol for Comprehensive Vascular Cleaning: Nanoparticle-Mediated Plaque Dissolution, Stem Cell Regeneration, and Chelation Maintenance -- A Rigorous Mathematical and Simulation-Based Framework
A Novel Multi-Stage Conceptual Protocol for Comprehensive Vascular Cleaning: Nanoparticle-Mediated Plaque Dissolution, Stem Cell Regeneration, and Chelation Maintenance -- A Rigorous Mathematical and Simulation-Based Framework Open
Atherosclerosis remains a leading global cause of cardiovascular morbidity and mortality, characterized by the accumulation of lipid plaques, fibrous deposits, and calcifications within arterial walls. This manuscript introduces a groundbr…
View article: Empirical Verification of the Scaling Law ℓ ∝ ρ_eff^{−β} in the Framework of Emergent Tensor Gravity: A Detailed Analysis of Observational Profiles
Empirical Verification of the Scaling Law ℓ ∝ ρ_eff^{−β} in the Framework of Emergent Tensor Gravity: A Detailed Analysis of Observational Profiles Open
This study presents a detailed empirical analysis of the scaling law ℓ ∝ ρ_eff^{−β}, a key prediction of the Emergent Tensor Gravity (ETG) framework with a theoretical exponent of β_theor = 0.5. The analysis is based on real observational …
View article: Empirical Verification of the Scaling Law ℓ ∝ ρ_eff^{−β} in the Framework of Emergent Tensor Gravity: A Detailed Analysis of Observational Profiles
Empirical Verification of the Scaling Law ℓ ∝ ρ_eff^{−β} in the Framework of Emergent Tensor Gravity: A Detailed Analysis of Observational Profiles Open
This study presents a detailed empirical analysis of the scaling law ℓ ∝ ρ_eff^{−β}, a key prediction of the Emergent Tensor Gravity (ETG) framework with a theoretical exponent of β_theor = 0.5. The analysis is based on real observational …
View article: A Novel Multi-Stage Conceptual Protocol for Comprehensive Vascular Cleaning: Nanoparticle-Mediated Plaque Dissolution, Stem Cell Regeneration, and Chelation Maintenance -- A Rigorous Mathematical and Simulation-Based Framework
A Novel Multi-Stage Conceptual Protocol for Comprehensive Vascular Cleaning: Nanoparticle-Mediated Plaque Dissolution, Stem Cell Regeneration, and Chelation Maintenance -- A Rigorous Mathematical and Simulation-Based Framework Open
Atherosclerosis remains a leading global cause of cardiovascular morbidity and mortality, characterized by the accumulation of lipid plaques, fibrous deposits, and calcifications within arterial walls. This manuscript introduces a groundbr…
View article: A Theoretical Bayesian SIR Modeling Framework for Burnout Propagation in Medical Education: Calibrated Simulations, Hierarchical Inference, and Intervention Projections
A Theoretical Bayesian SIR Modeling Framework for Burnout Propagation in Medical Education: Calibrated Simulations, Hierarchical Inference, and Intervention Projections Open
Burnout affects 37.23% (95% CI: 32.66--42.05%) of medical undergraduates globally \citep{almutairi2022prevalence}, rising to 44.2% before residency \citep{frajerman2019burnout} and threatening workforce sustainability. This theoretical man…
View article: METRIS: A 17-Agent Multi-Modal Framework for Quantitative AI Governance Assessment Using Bayesian Scoring, Monte Carlo Risk Simulation, and Ensemble Time Series Forecasting
METRIS: A 17-Agent Multi-Modal Framework for Quantitative AI Governance Assessment Using Bayesian Scoring, Monte Carlo Risk Simulation, and Ensemble Time Series Forecasting Open
As artificial intelligence systems proliferate across regulated industries, organizations face mounting pressure to demonstrate compliance with emerging frameworks including the EU AI Act, NIST AI RMF, and ISO/IEC 42001. We present METRIS …
View article: A Decision Tree-Based Cloud Replication Model for Enhanced Data Management
A Decision Tree-Based Cloud Replication Model for Enhanced Data Management Open
This paper introduces the Decision Tree Cloud Replication (DTCR) model, a novel Artificial Intelligence (AI)-based approach for managing data replication in cloud environments. The model is designed to enhance data availability, optimize p…
View article: A Theoretical Bayesian SIR Modeling Framework for Burnout Propagation in Medical Education: Calibrated Simulations, Hierarchical Inference, and Intervention Projections
A Theoretical Bayesian SIR Modeling Framework for Burnout Propagation in Medical Education: Calibrated Simulations, Hierarchical Inference, and Intervention Projections Open
Burnout affects 37.23% (95% CI: 32.66--42.05%) of medical undergraduates globally \citep{almutairi2022prevalence}, rising to 44.2% before residency \citep{frajerman2019burnout} and threatening workforce sustainability. This theoretical man…
View article: METRIS: A 17-Agent Multi-Modal Framework for Quantitative AI Governance Assessment Using Bayesian Scoring, Monte Carlo Risk Simulation, and Ensemble Time Series Forecasting
METRIS: A 17-Agent Multi-Modal Framework for Quantitative AI Governance Assessment Using Bayesian Scoring, Monte Carlo Risk Simulation, and Ensemble Time Series Forecasting Open
As artificial intelligence systems proliferate across regulated industries, organizations face mounting pressure to demonstrate compliance with emerging frameworks including the EU AI Act, NIST AI RMF, and ISO/IEC 42001. We present METRIS …
View article: Monotone data augmentation algorithm for longitudinal continuous, binary and ordinal outcomes: a unifying approach
Monotone data augmentation algorithm for longitudinal continuous, binary and ordinal outcomes: a unifying approach Open
The monotone data augmentation (MDA) algorithm has been widely used to impute missing data for longitudinal continuous outcomes. Compared to a full data augmentation approach, the MDA scheme accelerates the mixing of the Markov chain, redu…
View article: Monotone data augmentation algorithm for longitudinal continuous, binary and ordinal outcomes: a unifying approach
Monotone data augmentation algorithm for longitudinal continuous, binary and ordinal outcomes: a unifying approach Open
The monotone data augmentation (MDA) algorithm has been widely used to impute missing data for longitudinal continuous outcomes. Compared to a full data augmentation approach, the MDA scheme accelerates the mixing of the Markov chain, redu…
View article: The Inherited Matter Cosmology: A Unified, Falsifiable Solution to Low-z Cosmological Tensions
The Inherited Matter Cosmology: A Unified, Falsifiable Solution to Low-z Cosmological Tensions Open
This report presents the first complete formulation of the Inherited Matter Cosmology (IMC), a physically motivated alternative to ΛCDM that simultaneously addresses multiple observational tensions in modern cosmology. The work develops th…
View article: Exponentially Convergent Monte Carlo Finite Elements via Spectral Regularization
Exponentially Convergent Monte Carlo Finite Elements via Spectral Regularization Open
This paper introduces a novel numerical methodology for solving high-dimensional partial differential equations (PDEs) with stochastic inputs or high-dimensional parameter spaces, combining the robustness of Monte Carlo methods with the ge…
View article: On the efficiency of parameter space exploration: A scotogenic case study
On the efficiency of parameter space exploration: A scotogenic case study Open
A common problem in beyond Standard Model phenomenology is the exploration of a multi-dimensional parameter space in view of a large number of constraints. We study and compare two methods applicable to this challenge, namely a Markov Chai…
View article: On the efficiency of parameter space exploration: A scotogenic case study
On the efficiency of parameter space exploration: A scotogenic case study Open
A common problem in beyond Standard Model phenomenology is the exploration of a multi-dimensional parameter space in view of a large number of constraints. We study and compare two methods applicable to this challenge, namely a Markov Chai…
View article: The Inherited Matter Cosmology: A Unified, Falsifiable Solution to Low-z Cosmological Tensions
The Inherited Matter Cosmology: A Unified, Falsifiable Solution to Low-z Cosmological Tensions Open
This report presents the first complete formulation of the Inherited Matter Cosmology (IMC), a physically motivated alternative to ΛCDM that simultaneously addresses multiple observational tensions in modern cosmology. The work develops th…
View article: Iterative Causal Refinement: Computational Experiments and Robust Simulation Framework
Iterative Causal Refinement: Computational Experiments and Robust Simulation Framework Open
This ZIP archive contains the complete supplementary materials supporting the current development of the Inherited Matter Cosmology (IMC) framework.Included resources cover model calibration, parameter validation, and comparative analyses …
View article: Iterative Causal Refinement: Computational Experiments and Robust Simulation Framework
Iterative Causal Refinement: Computational Experiments and Robust Simulation Framework Open
This ZIP archive contains the complete supplementary materials supporting the current development of the Inherited Matter Cosmology (IMC) framework.Included resources cover model calibration, parameter validation, and comparative analyses …
View article: Relativistic Tensor Gravity (RTG): An Emergent Gravity Framework Without Dark Matter, Tested Against SPARC Galaxy Rotation Curves
Relativistic Tensor Gravity (RTG): An Emergent Gravity Framework Without Dark Matter, Tested Against SPARC Galaxy Rotation Curves Open
The ΛCDM cosmological model faces persistent challenges on galactic scales, including the core-cusp problem and rotation curve diversity. We present Relativistic Tensor Gravity (RTG), an emergent gravity framework based on spacetime emerge…
View article: Stochastic-Deterministic Fusion: Unifying Monte Carlo, Spectral, and Finite Element Methods for High-Dimensional Analysis
Stochastic-Deterministic Fusion: Unifying Monte Carlo, Spectral, and Finite Element Methods for High-Dimensional Analysis Open
High-dimensional problems are ubiquitous in science and engineering, posing significant computational challenges for traditional numerical methods. This paper proposes a novel Stochastic-Deterministic Fusion framework that unifies Monte Ca…
View article: Exponentially Convergent Monte Carlo Finite Elements via Spectral Regularization
Exponentially Convergent Monte Carlo Finite Elements via Spectral Regularization Open
This paper introduces a novel numerical methodology for solving high-dimensional partial differential equations (PDEs) with stochastic inputs or high-dimensional parameter spaces, combining the robustness of Monte Carlo methods with the ge…