Placing evolutionary events in the context of geological time is a fundamental goal in paleobiology and macroevolution. In this Element we describe the tripartite model used for Bayesian estimation of time calibrated phylogenetic trees. The model can be readily separated into its component models: the substitution model, the clock model and the tree model. We provide an overview of the most widely used models for each component and highlight the advantages of implementing the tripartite model within a Bayesian framework.
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Paperback. Condition: New. Placing evolutionary events in the context of geological time is a fundamental goal in paleobiology and macroevolution. In this Element we describe the tripartite model used for Bayesian estimation of time calibrated phylogenetic trees. The model can be readily separated into its component models: the substitution model, the clock model and the tree model. We provide an overview of the most widely used models for each component and highlight the advantages of implementing the tripartite model within a Bayesian framework. Seller Inventory # LU-9781108949422
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Paperback. Condition: new. Paperback. Placing evolutionary events in the context of geological time is a fundamental goal in paleobiology and macroevolution. In this Element we describe the tripartite model used for Bayesian estimation of time calibrated phylogenetic trees. The model can be readily separated into its component models: the substitution model, the clock model and the tree model. We provide an overview of the most widely used models for each component and highlight the advantages of implementing the tripartite model within a Bayesian framework. We describe the tripartite model used for Bayesian estimation of time calibrated phylogenetic trees. We provide an overview of the most widely used models for each component (the substitution, clock and tree models) and highlight the advantages of implementing the tripartite model within a Bayesian framework. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781108949422
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