Cited by 190Genomics and Phylogenetic StudiesMarkov Chains and Monte Carlo MethodsGenetic diversity and population structure

The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.

Identifiability of Parameters in MCMC Bayesian Inference of Phylogeny

Rannala, Bruce·2002·Systematic Biology

DOI 10.1080/10635150290102429

Open this paper in CoChat — free account

Abstract

Methods for Bayesian inference of phylogeny using DNA sequences based on Markov chain Monte Carlo (MCMC) techniques allow the incorporation of arbitrarily complex models of the DNA substitution process, and other aspects of evolution. This has increased the realism of models, potentially improving the accuracy of the methods, and is largely responsible for their recent popularity. Another consequence of the increased complexity of models in Bayesian phylogenetics is that these models have, in several cases, become overparameterized. In such cases, some parameters of the model are not identifiable; different combinations of nonidentifiable parameters lead to the same likelihood, making it impossible to decide among the potential parameter values based on the data. Overparameterized models can also slow the rate of convergence of MCMC algorithms due to large negative correlations among parameters in the posterior probability distribution. Functions of parameters can sometimes be found, in overparameterized models, that are identifiable, and inferences based on these functions are legitimate. Examples are presented of overparameterized models that have been proposed in the context of several Bayesian methods for inferring the relative ages of nodes in a phylogeny when the substitution rate evolves over time.

Integrity signals

Registry

Crossref · 10.1080/10635150290102429

Cites

17 of 17 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195
Statistical Inference
Casella, G., & Berger, R. L. (1993) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003456285 · Cited by 5,835
Bayes and Empirical Bayes Methods for Data Analysis
Carlin, B. P., & Louis, T. A. (2001) · Technometrics · doi:10.1198/tech.2001.s608 · Cited by 2,110
Introduction to Metropolis, Rosenbluth, Rosenbluth, Teller, and Teller (1953) Equations of State Calculations by Fast Computing Machines. J. Chem. Phys.,21, 1087–1092. and Geman and Geman (1984) Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Trans. Pattern Anal. Machine Intelligence,6, 721–741.
Huber, P. J. (1997) · Springer series in statistics · doi:10.1007/978-1-4612-0667-5_6 · Cited by 1,569
Monte Carlo Sampling Methods Using Markov Chains and Their Applications
Hastings, W. K. (1970) · Biometrika · doi:10.2307/2334940 · Cited by 1,526
Estimating the rate of evolution of the rate of molecular evolution
Thorne, J. L., Kishino, H., & Painter, I. S. (1998) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a025892 · Cited by 1,317
Bayesian phylogenetic inference using DNA sequences: a Markov Chain Monte Carlo Method
Yang, Z., & Rannala, B. (1997) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a025811 · Cited by 1,274
Self-citation
A nonparametric approach to estimating divergence times in the absence of rate constancy.
Sanderson, M. J. (1997) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a025731 · Cited by 1,156
Introducing Markov chain Monte Carlo
Gilks, W., Richardson, S., & Spiegelhalter, D. (1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835-6 · Cited by 671

Show all 17 references

Sorted by citations

Items 101–190 of 190

Bayesian inference for unidirectional misclassification of a binary response trait
Xia, M., & Gustafson, P. (2017) · Statistics in Medicine · doi:10.1002/sim.7555 · Cited by 13
Joint two‐part Tobit models for longitudinal and time‐to‐event data
Dagne, G. A. (2017) · Statistics in Medicine · doi:10.1002/sim.7429 · Cited by 12
Bayesian Inference of Phylogenetics Revisited: Developments and Concerns
Randle, C. P., Mort, M. E., & Crawford, D. J. (2005) · Taxon · doi:10.2307/25065298 · Cited by 12
Inference of strength and phase transition kinetics in dynamically-compressed tin
Schill, W. J., Schmidt, K., Austin, R., Anderson, W. W., Belof, J. L., Brown, J. L., & Barton, N. R. (2023) · Journal of Applied Physics · doi:10.1063/5.0150749 · Cited by 11
Realistic Gene Transfer to Gene Duplication Ratios Identify Different Roots in the Bacterial Phylogeny Using a Tree Reconciliation Method
Bremer, N., Knopp, M. R., Martin, W. F., & Tria, F. D. K. (2022) · Life · doi:10.3390/life12070995 · Cited by 10
Effectiveness of the BNT162b2 (Pfizer-BioNTech) and the ChAdOx1 nCoV-19 (Oxford-AstraZeneca) vaccines for reducing susceptibility to infection with the Delta variant (B.1.617.2) of SARS-CoV-2
Pattni, K., Hungerford, D., Adams, S., Buchan, I. E., Cheyne, C. P., García‐Fiñana, M., Hall, I., Hughes, D. M., Overton, C. E., Zhang, X. N., et al. (2022) · BMC Infectious Diseases · doi:10.1186/s12879-022-07239-z · Cited by 11
New Product Development in an Emerging Economy: Analysing the Role of Supplier Involvement Practices by Using Bayesian Markov Chain Monte Carlo Technique
Kanapathy, K., Khong, K. W., & Dekkers, R. (2014) · Journal of Applied Mathematics · doi:10.1155/2014/542606 · Cited by 10
Neutral and adaptive protein evolution
Yang, Z. (2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198567028.003.0008 · Cited by 10
Understanding mammalian evolution using Bayesian phylogenetic inference
Kelly, C. D. (2005) · Mammal Review · doi:10.1111/j.1365-2907.2005.00064.x · Cited by 10
A Bayesian Approach for Detecting Mass-Extinction Events When Rates of Lineage Diversification VaryPreprint
May, M. R., Höhna, S., & Moore, B. R. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/020149 · Cited by 9
Phylogeny and biogeography of harmochirine jumping spiders (Araneae: Salticidae)
Azevedo, G. H. F., Hedin, M. C., & Maddison, W. P. (2024) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2024.108109 · Cited by 8
The value of a datum – how little data do we need for a quantitative risk analysis?
Leung, B., & Steele, R. J. (2013) · Diversity and Distributions · doi:10.1111/ddi.12062 · Cited by 8
Extensions of the skew-normal ogive item response model
Bazán, J. L., Branco, M. D., & Bolfarine, H. (2014) · Brazilian Journal of Probability and Statistics · doi:10.1214/12-bjps191 · Cited by 8
Improving phylogenetic analyses by incorporating additional information from genetic sequence databases
Liang, L.-J. F., Weiss, R. E., Redelings, B. D., & Suchard, M. A. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp473 · Cited by 8
Population pharmacokinetic–pharmacodynamic modeling of PB2452, a monoclonal antibody fragment being developed as a ticagrelor reversal agent, in healthy volunteers
Kathman, S. J., Wheeler, J. J., Bhatt, D. L., Arnold, S. E., & Lee, J. S. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12734 · Cited by 8
Advances, challenges and opportunities of phylogenetic and social network analysis using COVID-19 data
Wang, Y., Zhao, Y., & Pan, Q. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab406 · Cited by 7
Assessing uncertainties and identifiability of foam displacement models employing different objective functions for parameter estimation
Valdez, A., Rocha, B. M., Chapiro, G., & dos Santos, R. W. (2022) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2022.110551 · Cited by 7
Simulating molecular evolution
Yang, Z. (2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198567028.003.0009 · Cited by 7
Patterns of interdivision time correlations reveal hidden cell cycle factors
Hughes, F. A., Barr, A. R., & Thomas, P. (2022) · eLife · doi:10.7554/elife.80927 · Cited by 7
Limited effects of among-lineage rate variation on the phylogenetic performance of molecular markers
Schwartz, R. S., & Mueller, R. L. (2010) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2009.12.025 · Cited by 6
Uncertainty quantification for model parameters and hidden state variables in Bayesian dynamic linear models
Nguyen, L. H., Gaudot, I., & Goulet, J. (2018) · Structural Control and Health Monitoring · doi:10.1002/stc.2309 · Cited by 6
Hynobiidae origin in middle Cretaceous corroborated by the new mitochondrial genome of Hynobius chinensis
Tang, D., Xu, T., & Sun, Y. (2015) · Marine Genomics · doi:10.1016/j.margen.2015.03.006 · Cited by 6
Structure-based methods for the phylogenetic analysis of ribosomal RNA molecules
Gillespie, J. J. (2005) · Cited by 6
Tracking niche change through time: simultaneous inference of ecological niche evolution and estimation of contemporary nichesPreprint
Hua, X., Cardillo, M., & Bromham, L. D. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.29.890608 · Cited by 6
Bayesian Selection of Relaxed-Clock Models: Distinguishing between Independent and Autocorrelated Rates
Panchaksaram, M., Freitas, L. A., & dos Reis, M. (2024) · Systematic Biology · doi:10.1093/sysbio/syae066 · Cited by 6
Models of nucleotide substitution
Yang, Z. (2014) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199602605.003.0001 · Cited by 5
Improved MCMC Simulation for Low-Dimensional Multi-Modal Distributions
Ji, H., Lee, J., & Kim, N. (2013) · Management Science and Financial Engineering · doi:10.7737/msfe.2013.19.2.049 · Cited by 4
The Concept of the Modern Molecular Clock and Experience in Estimating Divergence Times of Eulipotyphla and Rodentia
Bannikova, A. A., & Lebedev, V. S. (2022) · Biology Bulletin Reviews · doi:10.1134/s2079086422050024 · Cited by 4
Maximum likelihood methods
Yang, Z. (2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198567028.003.0004 · Cited by 4
Bayesian calibration of simple forest models with multiplicative mathematical structure: A case study with two Light Use Efficiency models in an alpine forest
Bagnara, M., Oijen, M. A. V., Cameron, D., Gianelle, D., Magnani, F., & Sottocornola, M. (2018) · Ecological Modelling · doi:10.1016/j.ecolmodel.2018.01.014 · Cited by 4
New probabilistic inference algorithms that harness the strengths of variational and Monte Carlo methods
Carbonetto, P. S. (2009) · cIRcle (University of British Columbia) · doi:10.14288/1.0051537 · Cited by 4
An Evolving View of Species Tree Inference
Kubatko, L. S. (2026) · Systematic Biology · doi:10.1093/sysbio/syag002 · Cited by 3
Bayesian Identifiability Analysis for Infectious Disease Models: Parameter Reduction and Model Selection
Wang, X. (2026) · Bulletin of Mathematical Biology · doi:10.1007/s11538-026-01596-5 · Cited by 3
Morphological largess: can morphology offer more and be modelled as a stochastic evolutionary process?
Larsson, H. C. E., Dececchi, T. A., & Harrison, L. B. (2012) · Cambridge University Press eBooks · doi:10.1017/cbo9780511760174.004 · Cited by 3
Phylogeny reconstruction: overview
Yang, Z. (2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198567028.003.0003 · Cited by 4
Maximum likelihood estimation of Micro-Doppler parameters based on MCMC
Hu, Y., Guo, L., Dong, X., & Xu, S. (2016) · Ubiquitous Positioning, Indoor Navigation, and Location Based Service · doi:10.1109/upinlbs.2016.7809983 · Cited by 3
Parameter Identifiability for a Profile Mixture Model of Protein Evolution
Yourdkhani, S., Allman, E. S., & Rhodes, J. A. (2021) · Journal of Computational Biology · doi:10.1089/cmb.2020.0315 · Cited by 3
An information theory approach to hypothesis testing in criminological research
Petrossian, G., & Maxfield, M. (2018) · Crime Science · doi:10.1186/s40163-018-0077-5 · Cited by 3
Using collateral information in the estimation of sub-scores --- a fully Bayesian approach
Tao, S., Vispoel, W. P., Ansley, T. N., Lee, W., Welch, C. J., & Cowles, M. K. (2009) · doi:10.17077/etd.e2hn8ux5 · Cited by 3
Assessing Ecosystem State Space Models: Identifiability and Estimation
Smith, J. W., Johnson, L. R., & Thomas, R. Q. (2023) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1007/s13253-023-00531-8 · Cited by 2
Neutral and adaptive protein evolution
Yang, Z. (2014) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199602605.003.0011 · Cited by 2
A switching Markov Chain Monte Carlo method for statistical identifiability of nonlinear pharmacokinetics models
Kim, S., & Li, L. (2011) · Statistica Sinica · doi:10.5705/ss.2010.263 · Cited by 2
Computational Approaches for Developing Informative Prior Distributions for Bayesian Calibration of PBPK Models
Davis, J. L., Tornero‐Velez, R., & Setzer, R. W. (2012) · ACS symposium series · doi:10.1021/bk-2012-1099.ch018 · Cited by 2
Bayesian Sensitivity Analysis for Non-ignorable Missing Data in Longitudinal Studies
Li, T., Somers, J. M., Hu, X. J., & McCandless, L. C. (2019) · Statistics in Biosciences · doi:10.1007/s12561-019-09234-6 · Cited by 2
A Method for Parameters Estimation in a Dynamical Model of Ebola Virus Transmission in Sierra Leone
Li, L., Du, L.-X., Yan, Z., Zhang, J., & Wu, Y.-P. (2020) · Complexity · doi:10.1155/2020/9172835 · Cited by 2
Simulating molecular evolution
Yang, Z. (2014) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199602605.003.0012 · Cited by 2
A user-friendly forest model with a multiplicative mathematical structure: a Bayesian approach to calibrationPreprint
Bagnara, M., Oijen, M. A. V., Cameron, D., Gianelle, D., Magnani, F., & Sottocornola, M. (2014) · doi:10.5194/gmdd-7-6997-2014 · Cited by 2
Models of amino acid and codon substitution
Yang, Z. (2014) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199602605.003.0002 · Cited by 1
Principal Components Analysis Fails to Recover Phylogenetic Structure in Hominins
Raskin, L. Y., Šešelj, M., Bitarello, B. D., Stroustrup, S., Li, J. K., & Huelsenbeck, J. P. (2026) · American Journal of Biological Anthropology · doi:10.1002/ajpa.70306 · Cited by 1
Improved Bayesian parameters estimation with model-based design of experiments for over-parameterized systems
Cao, X., Chen, X., & Biegler, L. T. (2025) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2025.109296 · Cited by 1
Complete species-level phylogeny of Madagascar’s tufted-tailed rats, Eliurus (Rodentia: Nesomyidae) reveals hidden diversity
Ilarde, G. C. T., Goodman, S. M., Soarimalala, V., & Jansa, S. A. (2025) · Zoological Journal of the Linnean Society · doi:10.1093/zoolinnean/zlaf191 · Cited by 1
Models of amino acid and codon substitution
Yang, Z. (2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198567028.003.0002 · Cited by 1
Coalescent theory and species trees
Yang, Z. (2014) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199602605.003.0009 · Cited by 1
GPU Acceleration for Markov Chain Monte Carlo Sampling
Li, J., Landry, S. J., & Mettu, R. R. (2024) · International Conference on AI-ML Systems (AIMLSystems) · doi:10.1145/3703412.3703428 · Cited by 1
Models of nucleotide substitution
Yang, Z. (2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198567028.003.0001 · Cited by 1
Bayesian methods
Yang, Z. (2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198567028.003.0005 · Cited by 1
An Investigation of the Phylogeny and Evolutionary Processes of Deliquescent Fruiting Bodies in the Mushroom Family Psathyrellaceae (Agaricales)
Nagy, L. G. (2012) · doi:10.14232/phd.1341 · Cited by 1
Tree Evaluation and Robustness Testing
Mariadassou, M., Bar‐Hen, A., & Kishino, H. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20259-8 · Cited by 1
On the Evolution of Kangaroos and their Kin (Family Macropodidae) Using Retrotransposons, Nuclear Genes and Whole Mitochondrial Genomes
Dodt, W. G. (2018) · Queensland University of Technology · doi:10.5204/thesis.eprints.116286 · Cited by 1
Maximum likelihood methods
Yang, Z. (2014) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199602605.003.0004 · Cited by 1
The evolution of mitochondrial genomes and phylogenetic relationships in the hymenoptera
Castro, L. R. (2006) · Research Online (University of Wollongong)
Blood-Oxygen-Level-Dependent Parameter Identification using Multimodal Neuroimaging and Particle Filters
Mundle, A. R. (2012) · VTechWorks (Virginia Tech)
THE PROTISTAN ORIGINS OF MULTICELLUARITY: TIMING AND EVOLUTION OF CELL ADHESION MOLECULES
Sharpe, S. C. (2015) · DalSpace (Dalhousie University)
Comparison of methods and tests on trees
Yang, Z. (2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198567028.003.0006
Bayesian computation (MCMC)
Yang, Z. (2014) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199602605.003.0007 · Cited by 1
Comparison of phylogenetic methods and tests on trees
Yang, Z. (2014) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199602605.003.0005
Molecular clock and estimation of species divergence times
Yang, Z. (2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198567028.003.0007
Contribuições ao estudo de variáveis dependentes limitadas contínuas e categóricas
Bazán, J. L. (2017) · doi:10.11606/t.55.2017.tde-20012017-095647
Bayesian theory
Yang, Z. (2014) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199602605.003.0006
Perspectives
Yang, Z. (2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198567028.003.0010
Appendices
Yang, Z. (2014) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199602605.003.0013
Molecular clock and estimation of species divergence times
Yang, Z. (2014) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199602605.003.0010
Bayesian phylogenetics
Yang, Z. (2014) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199602605.003.0008
Peer Review #2 of "Climatic niche evolution in the viviparous Sceloporus torquatus group (Squamata: Phrynosomatidae) (v0.1)"
(2019) · PeerJ · doi:10.7287/peerj.6192v0.1/reviews/2
Phylogeny reconstruction: overview
Yang, Z. (2014) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199602605.003.0003
Peer Review #2 of "Climatic niche evolution in the viviparous Sceloporus torquatus group (Squamata: Phrynosomatidae) (v0.3)"
(2019) · PeerJ · doi:10.7287/peerj.6192v0.3/reviews/2
Peer Review #1 of "Climatic niche evolution in the viviparous Sceloporus torquatus group (Squamata: Phrynosomatidae) (v0.1)"
(2019) · PeerJ · doi:10.7287/peerj.6192v0.1/reviews/1
Peer Review #3 of "Climatic niche evolution in the viviparous Sceloporus torquatus group (Squamata: Phrynosomatidae) (v0.1)"
(2019) · PeerJ · doi:10.7287/peerj.6192v0.1/reviews/3
Peer Review #1 of "Climatic niche evolution in the viviparous Sceloporus torquatus group (Squamata: Phrynosomatidae) (v0.2)"
(2019) · PeerJ · doi:10.7287/peerj.6192v0.2/reviews/1
Copyright Page
(2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198567028.002.0004
Appendices
(2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198567028.005.0001
Dedication
(2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198567028.002.0005
Preface
(2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198567028.002.0006
Preface
(2014) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199602605.002.0005
Copyright Page
(2014) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199602605.002.0003
Foreword
(2014) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199602605.002.0004
Mathematical modelling approach to investigate transmission dynamics of covid-19 with some control parameters
Paul, J. N. (2022) · doi:10.58694/20.500.12479/1632
Tree Evaluation and Robustness Testing
Mariadassou, M., Bar‐Hen, A., & Kishino, H. (2019) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00323-7
Additional file 1 of Effectiveness of the BNT162b2 (Pfizer-BioNTech) and the ChAdOx1 nCoV-19 (Oxford-AstraZeneca) vaccines for reducing susceptibility to infection with the Delta variant (B.1.617.2) of SARS-CoV-2
Pattni, K., Hungerford, D., Adams, S., Buchan, I. E., Cheyne, C. P., García-Fiñana, M., Hall, I., Hughes, D. M., Overton, C. E., Zhang, X., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19388922
Accelerating cardiovascular digital twin creation using precomputed virtual populations as Bayesian priors
Shelly, I. L., Peterson, J. N., Cornali, G., Rohrs, J., Gelbach, P. E., & Alexander, J. (2026) · Frontiers in Physiology · doi:10.3389/fphys.2026.1950271

Authors: Rannala, B.
Affiliations: University of Alberta (CA)
Corresponding author: Rannala, Bruce
ORCID: Rannala, Bruce
Venue: Systematic Biology
Publisher: Oxford University Press (OUP)
Published: 2002-09-01
Volume: 51
Issue: 5
Pages: 754-760
ISSN: 1063-5157 (ISSN-L)
1076-836X
DOI: 10.1080/10635150290102429
OpenAlex: W2107808362
Cited by: 190
References: 17
Citations by year: 2026: 4 · 2025: 2 · 2024: 4 · 2023: 2 · 2022: 8 · 2021: 6 · 2020: 4 · 2019: 16 · 2018: 7 · 2017: 5 · 2016: 5 · 2015: 9 · 2014: 23 · 2013: 8 · 2012: 13
FWCI: 2.09
Type: journal article
Language: en
Open access: closed
Locations: Systematic Biology (publishedVersion)
PubMed (publishedVersion)
Funders: National Institutes of Health
National Human Genome Research Institute (HG 01988, R01HG001988)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
Keywords: parameter identifiability, overparameterized models, Bayesian phylogenetics, MCMC convergence, node age estimation
MeSH terms: Algorithms, Bayes Theorem, Biological Evolution, Markov Chains, Models, Genetic, Monte Carlo Method, Phylogeny, Time Factors, Evolution, Molecular

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Identifiability of Parameters in MCMC Bayesian Inference of Phylogeny

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks