1 / 6

A Bayesian Network Model of Stromatolite Formation

A Bayesian Network Model of Stromatolite Formation. [Figure adapted from A. C. Allwood et al. Stromatolite reef from the Early Archaean era of Australia. Nature 441 (8 June 2006), 714-718.] Jack K. Horner Science Applications International Corporation jhorner@cybermesa.com.

harrison-bradley
Download Presentation

A Bayesian Network Model of Stromatolite Formation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A Bayesian Network Model of Stromatolite Formation [Figure adapted from A. C. Allwood et al. Stromatolite reef from the Early Archaean era of Australia. Nature 441 (8 June 2006), 714-718.] Jack K. Horner Science Applications International Corporation jhorner@cybermesa.com A Bayesian Network Model ...

  2. Problem statement • Stromatolites are attached, lithified sedimentary growth structures, accretionary away from a point or limited surface of initiation. • Whether stromatolites have a biotic origin is vigorously debated • If biotic in origin, the oldest (~3.5 billion years before present) were created by some of the first forms of terrestrial life • Because no single piece of evidence at present could decide whether stromatolites are of biotic origin, the debate depends significantly on how to interpret the “evidence as a whole” • How do we rigorously represent the notion of the “evidence as a whole”? A Bayesian Network Model ...

  3. Some requirements (Abstracted from Allwood et al., op. cit.) • Cone surfaces have a consistent/inconsistent vertical depth • There are systematic differences/similarities between the texture of the cone surfaces and the texture of the laminae between the cones • The cones are heterogeneously/homogeneously spaced • The cones are absent_from/present_in deep water • The cone surfaces exhibit/don’t_exhibit 250-fold enhanced rare earth element (REE) composition • The structure of the cone surfaces is consistent/inconsistent with the mat structure of several biotic sources • At many sites, individual instances of a given type of cone share/don’t_share common depositional characteristics, over an extended geographic region A Bayesian Network Model ...

  4. Implementation (Bayesian network)[Origin is the only hypothesis variable, all others are evidence variables. P(Origin = Biotic | X = “upper value”) = 0.9, where X ≠ Types_syndepositional is an evidence variable; else P(Origin = Biotic | X) ~ 0.1N, where N is number of types syndepositional. Argument from Allwood et al., op. cit., is shown.] A Bayesian Network Model ...

  5. Some results (sensitivity of Origin to evidence variables) A Bayesian Network Model ...

  6. Discussion • Many inference topologies are possible • at present, the literature does not motivate anything more complicated than the model shown above • the Bayesian network method can naturally accommodate more complexity if needed • Requirements do not uniquely determine the conditional probabilities • this is a common feature of scientific explanations • the Bayesian network methodallows us to rigorously compare effects of probability assignments (e.g., results are almost identical if P(Origin = Biotic | X = “upper value on Slide 4”) = 0.7 (instead of 0.9) A Bayesian Network Model ...

More Related