Re-pass the X-y pairs in the object
in a random (or specified) order to temporarily double the
size of the particle set
# S3 method for dynaTree
rejuvenate(object, odr = order(runif(length(object$y))),
verb = round(length(object$y)/10))The returned list is the same as dynaTree --
i.e., a "dynaTree"-class object but with 2 * object$N
particles. Note that object$N is not updated to reflect this
fact, but the C-side object will indeed have a double particle set.
Repeated calls to rejuvenate will cause the particle set to
double again.
a "dynaTree"-class object built by dynaTree
an integer vector of length(object$y) specifying the order
in which the object$X-object$y paris should be
processed for the rejuvenated particles
a positive scalar integer indicating how many time steps
(iterations) should pass before a progress statement is
printed to the console; a value of verb = 0 is quiet
Robert B. Gramacy rbg@vt.edu,
Matt Taddy and Christoforos Anagnostopoulos
The rejuvenate function causes the particle set to
temporarily double, to have size 2 * object$N. The new
object$N particles represent a discrete approximation
to the dynaTree posterior under the ordering specified
by odr, which may be random. Subsequent calls to
update.dynaTree cause the particle set to revert back
to object$N particles as only that many are obtained from
the particle learning resample step.
This function can be particularly useful in online learning contexts,
where retire.dynaTree is used to retain information
on discarded data, especially when the data is discarded historically
to deal with drifting concepts. Since the new, rejuvenated, particles
are based only on the active data, object$X-object$y
pairs (and not the retired data via informative leaf priors),
subsequent update.dynaTree steps allow the data
to dictate if old (informative prior) or new (default prior) particles
are best for the new concept
Taddy, M.A., Gramacy, R.B., and Polson, N. (2011). “Dynamic trees for learning and design” Journal of the American Statistical Association, 106(493), pp. 109-123; arXiv:0912.1586
Anagnostopoulos, C., Gramacy, R.B. (2013) “Information-Theoretic Data Discarding for Dynamic Trees on Data Streams.” Entropy, 15(12), 5510-5535; arXiv:1201.5568
Carvalho, C., Johannes, M., Lopes, H., and Polson, N. (2008). “Particle Learning and Smoothing”. Discussion Paper 2008-32, Duke University Dept. of Statistical Science.
dynaTree, alcX.dynaTree,
entropyX.dynaTree, update.dynaTree,
retire.dynaTree
## see retire.dynaTree for a combined example
## illustrating rejuvenation
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