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The Dicynodontia (Therapsida: Anomodontia) is one of the most successful Permo-Triassic terrestrial tetrapod clades and the oldest specimens are recorded from the middle PermianEodicynodonAssemblage Zone of South Africa. Their fossil record is abundant and species-rich across Pangea. By contrast, the fossil record of the basal-most anomodonts, which includes non-dicynodont anomodonts and early forms of dicynodonts, is patchy and their morphology and phylogeny are deduced from relatively few specimens. Discovered in 1982 and described in 1990, the holotype ofEodicynodon oelofseni(NMQR 2913) is one of the better-preserved early anomodont specimens. However, it has been suggested thatE. oelofsenidoes not belong to the genusEodicynodon. Here, using CT-scanning and 3D modeling, the skull ofEodicynodon oelofseni,Patranomodon nyaphuliiandEodicynodon oosthuizeniare redescribed. In the framework of this study, the application of 3D scanning technology to describe anatomical structures which were previously inaccessible in these fossils has enabled detailed redescription of the cranial morphology of the basal anomodontsPatranomodon,Eodicynodon oelofseniandE. oosthuizeniand led to a greater understanding of their cranial morphology and phylogenetic relationships. Based on an anatomical comparison and phylogenetic analyses (Bayesian and cladistics) the phylogenetic relationships of basal anomodonts are reassessed and it is suggested that NMQR 2913 does not belong to the genusEodicynodonbut likely represents a separate genus basal to other dicynodonts. A new genus is erected for NMQR 2913. This presents one of the first applications of Bayesian Inference of phylogeny on Therapsida. 

Phylogenetic estimation is, and has always been, a complex endeavor. Estimating a phylogenetic tree involves evaluating many possible solutions and possible evolutionary histories that could explain a set of observed data, typically by using a model of evolution. Values for all model parameters need to be evaluated as well. Modern statistical methods involve not just the estimation of a tree, but also solutions to more complex models involving fossil record information and other data sources. Markov chain Monte Carlo (MCMC) is a leading method for approximating the posterior distribution of parameters in a mathematical model. It is deployed in all Bayesian phylogenetic tree estimation software. While many researchers use MCMC in phylogenetic analyses, interpreting results and diagnosing problems with MCMC remain vexing issues to many biologists. In this manuscript, we will offer an overview of how MCMC is used in Bayesian phylogenetic inference, with a particular emphasis on complex hierarchical models, such as the fossilized birth-death (FBD) model. We will discuss strategies to diagnose common MCMC problems and troubleshoot difficult analyses, in particular convergence issues. We will show how the study design, the choice of models and priors, but also technical features of the inference tools themselves can all be adjusted to obtain the best results. Finally, we will also discuss the unique challenges created by the incorporation of fossil information in phylogenetic inference, and present tips to address them.