Results 51 to 60 of about 157,395 (292)

Bivariate Causal Discovery using Bayesian Model Selection [PDF]

Much of the causal discovery literature prioritises guaranteeing the identifiability of causal direction in statistical models. For structures within a Markov equivalence class, this requires strong assumptions which may not hold in real-world datasets ...
Dhir, Anish, Power, Samuel, van der Wilk, Mark   +2 more
core   +4 more sources

Universal Darwinism as a process of Bayesian inference

Frontiers in Systems Neuroscience, 2016
Many of the mathematical frameworks describing natural selection are equivalent to Bayes’ Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these ...
John Oberon Campbell
doaj   +1 more source

Bayesian model selection analysis of WMAP3 [PDF]

Physical Review D, 2006
We present a Bayesian model selection analysis of WMAP3 data using our code CosmoNest. We focus on the density perturbation spectral index $n_S$ and the tensor-to-scalar ratio $r$, which define the plane of slow-roll inflationary models. We find that while the Bayesian evidence supports the conclusion that $n_S \neq 1$, the data are not yet powerful ...
Parkinson, David, Mukherjee, Pia, Liddle, Andrew R.   +2 more
openaire   +5 more sources

Reconstructing enzyme evolution by protein engineering

FEBS Letters, EarlyView.
Natural enzyme evolution can be retraced by protein engineering methods such as directed evolution, rational design, and ancestral sequence reconstruction. These approaches reveal how enzymes emerged from ligand‐binding scaffolds, developed varying substrate preferences, formed oligomeric complexes, adapted to environmental changes, and evolved novel ...
Lukas Drexler, Markus R. Busch, Reinhard Sterner   +2 more
wiley   +1 more source

Sequential MCMC for Bayesian model selection

, 1999
In this paper, we address the problem of sequential Bayesian model selection. This problem does not usually admit any closed-form analytical solution.
Freitas Arnaud Doucet, Nando De, Christophe Andrieu   +2 more
core   +2 more sources

Bayesian model selection for exponential random graph models [PDF]

Social Networks, 2013
30 pages; Accepted to appear in Social ...
Alberto Caimo, Nial Friel
openaire   +4 more sources

Single‐cell DNA methylation profiling: Technologies, computation, and applications in precision oncology

Molecular Oncology, EarlyView.
Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley   +1 more source

Comparing and calibrating discrepancy measures for Bayesian model selection [PDF]

, 2020
Different approaches have been considered in the literature for the problem of Bayesian model selection.
María Teresa Rodríguez-Bernal, Julián De La Horra   +1 more
core  

Bayesian model selection based on proper scoring rules [PDF]

, 2015
Bayesian model selection with improper priors is not well-defined becauseof the dependence of the marginal likelihood on the arbitrary scaling constantsof the within-model prior densities.
MUSIO, MONICA, Philip Dawid, A.
core   +1 more source

Bayesian Variable Selection for Pareto Regression Models with Latent Multivariate Log Gamma Process with Applications to Earthquake Magnitudes

Geosciences, 2019
Generalized linear models are routinely used in many environment statistics problems such as earthquake magnitudes prediction. Hu et al. proposed Pareto regression with spatial random effects for earthquake magnitudes.
Hou-Cheng Yang, Guanyu Hu, Ming-Hui Chen
doaj   +1 more source