Retrodiction in Evolutionary Genomics: A Philosophical Perspective.

January 1, 2026

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Key Points

This work explores the principles of retrodiction and prediction in evolutionary genomics, focusing on conditional probabilities.
Examined the differences between forward-directed and backward-directed conditional probabilities.
Discussed the implications of Bayesian models in handling retrodiction and prediction.
Analyzed model selection theory and information degradation in causal chains.
Forward-directed models cannot retrodict while backward-directed models cannot predict.
Bayesian models can utilize both forward and backward probabilities effectively.
The rate of information loss in phylogenetic trees can be mitigated through branching processes.

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Philosophical insights reveal the dynamics of retrodiction and prediction in evolutionary genomics processes.

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Elliott Sober (2026) studied this question.

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