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Bayes' theorem

Definition
AI-generated

Identity relating posterior odds to prior odds times the likelihood ratio—conceptual backbone of Bayesian fine-mapping and colocalization priors.

Topics
Bayesian statistics
Related
Genome-Wide Association Study (GWAS) Linear Mixed Model Generalized Linear Mixed Model

Why it matters in GWAS

Statistical concepts underpin GWAS significance, effect estimation, relatedness random effects, multiple testing, fine-mapping priors, and post-GWAS multivariate methods.

Example usage

"Fine-mapping posteriors were updated via Bayes' theorem by combining functional priors with observed association likelihoods."

Genome-Wide Association Study (GWAS) Linear Mixed Model Generalized Linear Mixed Model Statistical Power Effect Size Bonferroni Correction False Discovery Rate Colocalization Fine Mapping
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Casella G, Berger RL. (2002). Statistical Inference. Duxbury Press.
  • Wasserman L. (2004). All of Statistics. Springer.
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