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Possibly the reason for few responses is such data doesn’t apply to a
Bayesian analysis.
Of course my understanding of using Bayesian stats. is minimal. I see
little difference in collecting decay data continuously over a long period
and discontinuously as long as the counts are correctly time identified.
Ive been doing this for some time in an effort to identify the isotope(s)
in Salinas Valley agricultural “dust” collected from window filters in my
home.
Of course, as more data is collected the half life will become more
accurate, because the data observes a Poisson distribution.
bc …. doesn’t fear treading, and expects a final from JD. and comment
from AW.
p.s. Is PN using the 10’ count as an average for a single datum point
or, for example, ten one minute counts? I’ve not interpreted the Python,
as I don’t know Python, and only superficially C++, or for that matter,
BASIC, Algol, and Fortran 2.
On 2021/Sep/21, at 10:28, Paul Nord <Paul.Nord@valpo.edu> wrote:that
That title doesn't google well. It seems like there should be a good
reference for this. The best I've got is David MacKay's code here:
http://www.inference.org.uk/mackay/itprnn06/slides/10/mgp00010.html
Translated into python below.
Two questions:
1. If we record the number of counts observed with a geiger tube at
discrete periods within the decay, is this approach still valid? Say
I've got a sample with a 52 hour half life. I come back about once aday,
fire up the good old geiger tube and measure the activity for 10 minutes.function
Can I just use that number of counts as the power of a probability
to multiply through here?periods
2. Will this give me a good result if I'm extracting multiple decay
from the same data?
The half lives of neutron activated copper is the experiment, actually.
Paul
import matplotlib.pyplot as plt
import numpy as np
import math
a = 1.0
b = 20.0
def p(x,l):
val = math.exp( -x/l ) / Z(l)/l
return val
def Z(l):
val = math.exp( -a/l) - math.exp(-b/l)
return val
def like(l):
return p(3,l) * p(5,l) * p(12,l)
t1 = np.arange(0.01,1000.0,.1)
fig,axs = plt.subplots(2)
axs[0].semilogx()
axs[1].semilogx()
axs[0].plot(t1,[p(3,x) for x in t1])
axs[0].plot(t1,[p(5,x) for x in t1])
axs[0].plot(t1,[p(12,x) for x in t1])
axs[1].plot(t1,[like(x) for x in t1])
plt.show()
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