*Elliott Sober*

- Published in print:
- 2006
- Published Online:
- September 2007
- ISBN:
- 9780199297306
- eISBN:
- 9780191713729
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199297306.003.0003
- Subject:
- Biology, Evolutionary Biology / Genetics

The use of a principle of parsimony in phylogenetic inference is both widespread and controversial. It is controversial because biologists, who view phylogenetic inference as first and foremost a ...
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The use of a principle of parsimony in phylogenetic inference is both widespread and controversial. It is controversial because biologists, who view phylogenetic inference as first and foremost a statistical problem, have pressed the question of what one must assume about the evolutionary process if one is entitled to use parsimony in this way. They suspect, not just that parsimony makes assumptions about the evolutionary process, but that it makes highly specific assumptions that are often implausible. That it must make some assumptions seems clear to them because they are confident that the method of maximum parsimony must resemble the main statistical procedure used to make phylogenetic inferences: the method of maximum likelihood. Likelihoodists suspect that parsimony nonetheless involves an implicit model. The question for them is to discover what that model is. This chapter discusses parsimony's ostensive presuppositions by examining the relationship that exists between maximum likelihood and maximum parsimony among simple examples in which parsimony and likelihood disagree.Less

The use of a principle of parsimony in phylogenetic inference is both widespread and controversial. It is controversial because biologists, who view phylogenetic inference as first and foremost a statistical problem, have pressed the question of what one must assume about the evolutionary process if one is entitled to use parsimony in this way. They suspect, not just that parsimony makes assumptions about the evolutionary process, but that it makes highly specific assumptions that are often implausible. That it must make some assumptions seems clear to them because they are confident that the method of maximum parsimony must resemble the main statistical procedure used to make phylogenetic inferences: the method of maximum likelihood. Likelihoodists suspect that parsimony nonetheless involves an implicit model. The question for them is to discover what that model is. This chapter discusses parsimony's ostensive presuppositions by examining the relationship that exists between maximum likelihood and maximum parsimony among simple examples in which parsimony and likelihood disagree.

*Pablo A. Goloboff and Diego Pol*

- Published in print:
- 2006
- Published Online:
- September 2007
- ISBN:
- 9780199297306
- eISBN:
- 9780191713729
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199297306.003.0008
- Subject:
- Biology, Evolutionary Biology / Genetics

The intent of a statistically-based phylogenetic method is to estimate tree topologies and values of possibly relevant parameters, as well as the uncertainty inherent in those estimations. A method ...
More

The intent of a statistically-based phylogenetic method is to estimate tree topologies and values of possibly relevant parameters, as well as the uncertainty inherent in those estimations. A method that could do that with reasonable accuracy would be attractive indeed. It is often claimed that it is advantageous for a method to be based on a specific evolutionary model, because that allows incorporating into the analysis the ‘knowledge’ of the real world embodied in the model. Bayesian methods have become very prominent among model-based methods, in part because of computational advantages, and in part because they estimate the probability that a given hypothesis is true, given the observations and model assumptions. Through simulation studies, this chapter finds that even if there is the potential for Bayesian estimations of monophyly to provide correct topological estimations for infinite numbers of characters, the resulting claims to measure degrees of support for conclusions — in a statistical sense — are unfounded. Additionally, for large numbers of terminals, it is argued to be extremely unlikely that a search via Markov Monte Carlo techniques would ever pass through the optimal tree(s), let alone pass through the optimal tree(s) enough times to estimate their posterior probability with any accuracy.Less

The intent of a statistically-based phylogenetic method is to estimate tree topologies and values of possibly relevant parameters, as well as the uncertainty inherent in those estimations. A method that could do that with reasonable accuracy would be attractive indeed. It is often claimed that it is advantageous for a method to be based on a specific evolutionary model, because that allows incorporating into the analysis the ‘knowledge’ of the real world embodied in the model. Bayesian methods have become very prominent among model-based methods, in part because of computational advantages, and in part because they estimate the probability that a given hypothesis is true, given the observations and model assumptions. Through simulation studies, this chapter finds that even if there is the potential for Bayesian estimations of monophyly to provide correct topological estimations for infinite numbers of characters, the resulting claims to measure degrees of support for conclusions — in a statistical sense — are unfounded. Additionally, for large numbers of terminals, it is argued to be extremely unlikely that a search via Markov Monte Carlo techniques would ever pass through the optimal tree(s), let alone pass through the optimal tree(s) enough times to estimate their posterior probability with any accuracy.