We build on the view of the Exact Renormalization Group (ERG) as an instantiation of Optimal Transport described by a functional convection-diffusion equation. We provide a new information-theoretic perspective for understanding the ERG through the intermediary of Bayesian Statistical Inference. This connection is facilitated by the Dynamical Bayesian Inference scheme, which encodes Bayesian inference in the form of a one-parameter family of probability distributions solving an integro-differential equation derived from Bayes' law. In this note, we demonstrate how the Dynamical Bayesian Inference equation is, itself, equivalent to a diffusion equation, which we dub . By identifying the features that define Bayesian Diffusion and mapping them onto the features that define the ERG, we obtain a dictionary outlining how renormalization can be understood as the inverse of statistical inference.
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http://dx.doi.org/10.3390/e26050389 | DOI Listing |
Epigenomics
January 2025
NIHR Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, Southampton, UK.
Aim: We aim to assess association of DNA methylation (DNAm) at birth with total immunoglobulin E (IgE) trajectories from birth to late adolescence and whether such association is ethnicity-specific.
Methods: We examined the association of total IgE trajectories from birth to late adolescence with DNAm at birth in two independent birth cohorts, the Isle of wight birth cohort (IOWBC) in UK ( = 796; White) and the maternal and infant cohort study (MICS) in Taiwan ( = 60; Asian). Biological pathways and methylation quantitative trait loci (methQTL) for associated Cytosine-phosphate-Guanine sites were studied.
J Coll Physicians Surg Pak
January 2025
Department of Radiotherapy, Binhai County People's Hospital, Yancheng, Jiangsu, China.
Objective: To investigate the causal influence of gut microbiota on small cell lung cancer (SCLC) progression using Mendelian randomisation (MR), providing insights into the gut-lung axis in lung cancer pathology.
Study Design: Analytical study. Place and Duration of the Study: Department of Radiotherapy, Binhai County People's Hospital, Yancheng, Jiangsu, China, and Department of Paediatrics, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China, from January to May 2024.
J Affect Disord
January 2025
Department of Orthopaedics, Xiangya Hospital, Central South University, Changsha, Hunan Province, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan Province, China. Electronic address:
Background: Studies have demonstrated that the gut microbiome-immune system-brain axis plays an important role in neurological disorders. Furthermore, recent studies have shown that the gut microbiota influences the occurrence and progression of anxiety disorders, with potential involvement of immune cells. We aimed to investigate the causal impact of gut microbiota on anxiety disorders and identify potential immune cell mediators.
View Article and Find Full Text PDFAm J Hum Genet
January 2025
Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA; Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA. Electronic address:
Genetic summary data are broadly accessible and highly useful, including for risk prediction, causal inference, fine mapping, and incorporation of external controls. However, collapsing individual-level data into summary data, such as allele frequencies, masks intra- and inter-sample heterogeneity, leading to confounding, reduced power, and bias. Ultimately, unaccounted-for substructure limits summary data usability, especially for understudied or admixed populations.
View Article and Find Full Text PDFCureus
January 2025
General Practice, Wad Medani Hospital, Wad Medani, SDN.
To enhance patient outcomes in pediatric cancer, a better understanding of the medical and biological risk variables is required. With the growing amount of data accessible to research in pediatric cancer, machine learning (ML) is a form of algorithmic inference from sophisticated statistical techniques. In addition to highlighting developments and prospects in the field, the objective of this systematic study was to methodically describe the state of ML in pediatric oncology.
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