Sentences with phrase «calendar age distribution»

You finally filter out from the collection of trials all those with a particular measured C14 age, and look at the distributions of true calendar ages that generated it, compared against the measured calendar age distribution each algorithm output.

Not exact matches

The withdrawal rules are different for inherited IRA accounts; generally you must begin taking distributions from an inherited IRA in the calendar year following the year of the IRA owner's death, both for traditional and Roth inherited IRAs, regardless of your age.

Let us simplify the problem further and assume that the lab can estimate the RC age on a sample (given an assumed initial mass fraction) with negligible error, and then consider how we generate the probability distribution for calendar date even when we have no laboratory measurement error to take into account.

In effect, for each calendar date the constructors have built the conditional distribution of «RC age given Calendar Datecalendar date the constructors have built the conditional distribution of «RC age given Calendar DateCalendar Date».

The distribution for the measurement of carbon - 14 age has (we're assuming) the same standard deviation for every calendar year, so it's always that case that we get some particular carbon - 14 measurement that was «unlikely», since any particular value for the measurement error is unlikely.

Jeffreys» prior, which in effect converts length elements in 14C space to length elements in calendar age space, may convert single length elements in 14C space to multiple length elements in calendar age space when the same 14C age corresponds to multiple calendar ages, thus over-representing in the posterior distribution the affected parts of the 14C error distribution probability.

The problem is, what input distribution do you use in the first step, to generate the true calendar ages?

You submit a physical sample which has a particular true calendar / C14 age combination — a random point on the calibration curve — with some input distribution.

For each sampled true calendar age, a 14C determination age is sampled randomly from a Gaussian error distribution.

But the problem as I see it is that whilst it is probably realistic to assume that some kind of local uniformity results from that process, that doesn't tell you what calendar ages it spans nor what the shape of the distribution in that region is.

If the measurement for carbon - 14 age has Gaussian error with standard deviation 100 (as seems about right for Nic's Fig. 2), and the measurement is rounded to one decimal place, and the calibration curve maps calendar age 750 to carbon - 14 age 1000, then the probability of the observation being 1000.0 given that the calendar age is 750 is 0.1 (for one decimal place) times the probability density at 1000 of a Gaussian distribution with mean 1000 and standard deviation 100, which works out to 0.0004.

This can be demonstrated analytically directly from the definition of the Fisher Information for a single parameter space if a constant variance is assumed for the distribution of RC age given a calendar date, and the «true» calendar dates for testing are selected from a uniform distribution.

Most of the information in that distribution is from your knowledge of how the ages of archaeological artefacts are distributed, and in particular that they are likely to be more uniform in calendar age than C14 age.