Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Fast likelihood calculation for multivariate Gaussian phylogenetic models with shifts.

Theoretical population biology - 1 Feb 2020

Mitov Venelin, Bartoszek Krzysztof, Asimomitis Georgios, Stadler Tanja

Abstract excerpt

Phylogenetic comparative methods (PCMs) have been used to study the evolution of quantitative traits in various groups of organisms, ranging from micro-organisms to animal and plant species. A common approach has been to assume a Gaussian phylogenetic model for the trait evolution along the tree, such as a branching Brownian motion (BM) or an Ornstein-Uhlenbeck (OU) process. Then, the parameters of the process...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Bayes Theorem
Share to Likelihood Functions

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Models, Genetic

Share to Phenotype

Share to Phylogeny

Share to Probability

Source

Identifiers

PMID: 31805292
DOI: 10.1016/j.tpb.2019.11.005

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Fast likelihood calculation for multivariate Gaussian phylogenetic models with shifts.PMID 31805292Read ↗

Inference of Adaptive Shifts for Multivariate Correlated Traits.Semantic proximity Read ↗

Modeling Pulsed Evolution and Time-Independent Variation Improves the Confidence Level of Ancestral and Hidden State Predictions.Semantic proximity Read ↗

Of Traits and Trees: Probabilistic Distances under Continuous Trait Models for Dissecting the Interplay among Phylogeny, Model, and Data.Semantic proximity Read ↗

Quantifying and comparing phylogenetic evolutionary rates for shape and other high-dimensional phenotypic data.Semantic proximity Read ↗

Likelihood-Based Parameter Estimation for High-Dimensional Phylogenetic Comparative Models: Overcoming the Limitations of "Distance-Based" Methods.Semantic proximity Read ↗

Phylogenetics by likelihood: evolutionary modeling as a tool for understanding the genome.Semantic proximity Read ↗

A stochastic evolutionary model for protein structure alignment and phylogeny.Semantic proximity Read ↗

A likelihood framework to analyse phyletic patterns.Semantic proximity Read ↗

Phylogenetic analysis using Lévy processes: finding jumps in the evolution of continuous traits.Semantic proximity Read ↗

Bayesian phylogenetics with BEAUti and the BEAST 1.7.Semantic proximity Read ↗

A phylogenetic Kalman filter for ancestral trait reconstruction using molecular data.Semantic proximity Read ↗

An Integrated Model of Phenotypic Trait Changes and Site-Specific Sequence Evolution.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.