The left side shows a crude simulation of animals moving around, which are periodically captured. Colour indicates number of times captured. Right side is a distribution describing belief over the population size.
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To count an animal population if you can't catch them all: capture some, mark them, release, and capture again. The ratio of new vs already seen tells you something about the total number. Shown here is iterated mark-and-recapture with a Bayesian updates to belief about pop sizepic.twitter.com/jZF7HXddiq
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It's funny that the first 'measurement' is much less informative than subsequent ones: it just tells you that the population is at least as large as the number of animals you've captured.
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Assumption made here: the animals move around enough between measurements that you can treat each capture event as a random selection from the population, independent of the previous capture
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I’d like to read more about the math, where can I do that?
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I've written up some details here: http://www.awebb.info/blog/iterated_mark …
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Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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I'm interested in how you made your simulation! Custom code, use of a library, or done in an existing simulator?
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Matplotlib for plotting, Scipy for likelihood computation (but Scipy doesn't support multivariate hypergeometric distribution, so I had to build that out of the univariate case). There's some code (not for this exact simulation, but simpler) here: http://www.awebb.info/blog/iterated_mark …
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Could you explain why the estimate for the peak seems not to be converging very well? The spread is narrowing, but the mean gets worse.
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I think it's random, and that it would eventually converge, but I've not done enough simulations to be sure. As
@balzacdiet says it may be because the capture events are happening too frequently, not allowing the population to become mixed (as is assumed)https://twitter.com/balzacdiet/status/1212690827509665792 … - Još 2 druga odgovora
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Čini se da učitavanje traje već neko vrijeme.
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