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Basic age-modelling

Basic age-modelling. Find ages for dated and undated depths E.g., linear interpolation, regression, spline (gaps) Choose which one looks nicest... How treat point estimates? (mid/max, multimodal) ‏. Bayesian age-modelling. Bayesian = combine data with other info 14 C dates and depth info

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Basic age-modelling

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  1. Basic age-modelling • Find ages for dated and undated depths • E.g., linear interpolation, regression, spline (gaps) • Choose which one looks nicest... • How treat point estimates? (mid/max, multimodal)‏

  2. Bayesian age-modelling • Bayesian = combine data with other info • 14C dates and depth info • stratigraphical ordering / position • e.g., wiggle-match dating • Constraints on e.g. likely accumulation rates • Other dates, e.g. pollen events, 210Pb (…) • Outlier analysis • Usually done by millions of simulations

  3. Wiggle-match dating

  4. Outlier analysis • Reasons: site, error, lab? • Give prior outlier probabilities to dates • Iteration i: is date within 2 lengths sd? • If not, label date and shift to fit • [Labelled / total]  posterior outlier prob. • No need to remove outliers! • Fit F: 1 – mean(posterior outlier prob.)‏

  5. OxCal • Extract OxCal directory to C:\Program Files • Open .../OxCal/Index.html in Firefox • R_Date( “test”, 2450, 50); • Save file, run • Run examples from manual

  6. Bpeat • Extract Bpeat.zip somewhere • Open R there (or change dir) • source(“Bpeat.R”) • SetCore(“MSB2K”,2) • TestRun() • FinalRun( 0.1 ) # just a short run... • DepthChron()

  7. The future of Bpeat: Bacon

  8. Bacon • Muscles and fat – robust, yet flexible • Floppy/crusty – flexibility can be adapted • Can be cut to your liking – hiatuses • Cured – Bpeat bugs repaired • MacBacon – multi-platform • Pigs are smart – combine prior info + new data • Pigs can fly – workshop in Mexico

  9. Age-modelling … your own data? • Try the different software pieces • What are best settings for your site? • Do you agree with the age estimates? • Differences between approaches

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