Hi Erik,
your examples on H-bond acfs are interesting. I'm wondering about the
"distinct features which are non-exponential" in your examples. What do
you mean exactly? Could these features be due to rare (H-bonding)
events, or in other words to poor sampling?
Intuitively, I'd say that the interpretation of the acf (at least for
the decay and the decorelation) is always valid unless maybe if you have
very poor statistics.
Ciao,
Patrick
Le 20/05/2012 16:12, Erik Marklund a écrit :
Dear Chris,
As I see it there one can interpret the acf and correlatation time
further for certain types of data. I'll use the h-bond autocorrelation
function as an example. Here the data is time series of logical true and
false, represented as ones and zeros. This type of acf can be direcly
interpreted as a probablity, and some quantities derived from the acf
can bear further meaning because of this.
I also thought that the nature of the data may be such there is a
non-exponential part, which makes the autocorreltaion time less valid,
or less connected to other intuitive concepts. Again, the h-bond acf has
distinct features which are non-exponential and the autocorreltaion time
derived from such acfs may in fact be misleading when the h-bond
kinetics is to be determined.
Hope that makes sense. I's be happy to hear from you if you disagree.
Best,
Erik
19 maj 2012 kl. 04.01 skrev Christopher Neale:
Dear Erik:
I thought about your comment for a while and I have come to understand
that you are correct. The exponential (or integral) autocorrelation
time is a mathematical construct and is defined as such. What I was
looking for was an interpretation of the autocorrelation time in terms
of the time required to decorrelate the sampling.
As to whether or not this will depend on the nature of the data, I
don't really understand your conjecture. If the interpretation of the
autocorrelation time depends on the nature of the data, then that
implies to me that a single scalar value is useless in this case. I
don't understand how it could be useful to represent the
autocorrelation time by a single number if that number does not mean
anything on its own. If you have time, I would appreciate if you could
elaborate on this point.
Thank you,
Chris.
-- original message --
Aren't you looking for an interpretation rather than a definition? And
will this not depend on the nature of the data?
Best,
Erik
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Erik Marklund, PhD
Dept. of Cell and Molecular Biology, Uppsala University.
Husargatan 3, Box 596, 75124 Uppsala, Sweden
phone: +46 18 471 6688 fax: +46 18 511 755
er...@xray.bmc.uu.se <mailto:er...@xray.bmc.uu.se>
http://www2.icm.uu.se/molbio/elflab/index.html
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Patrick FUCHS
Dynamique des Structures et Interactions des Macromolécules Biologiques
INTS, INSERM UMR-S665, Université Paris Diderot,
6 rue Alexandre Cabanel, 75015 Paris
Tel : +33 (0)1-44-49-30-57 - Fax : +33 (0)1-43-06-50-19
E-mail address: patrick.fu...@univ-paris-diderot.fr
Web Site: http://www.dsimb.inserm.fr/~fuchs
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