View source: R/md_candidate_set_models.R
md_bernoulli_cand_C1_kld | R Documentation |
For each row (observation) in md
, a probability Q = (q1, q2, ..., qm)
is
constructed such that qj
is the probability that the j-th component is in
the candidate set, qk = 1
, where k
is failed component.
md_bernoulli_cand_C1_kld(
md,
p,
d,
eps = 1e-04,
max_iter = 100000L,
lr = 1,
lambda = 1,
alpha0 = 5,
beta0 = 0.5,
debug = F
)
md |
component failure times for the series system |
p |
numeric, defines |
d |
numeric, the KL divergence from P = (p, p, ..., p, 1, p, ..., p) to try to obtain |
eps |
numeric, stopping condition. |
max_iter |
Integer, maximum number of iterations before giving up. |
lr |
numeric, learning rate. |
lambda |
numeric, controls how much the two constraints are weighted.
Lower value specifies more enforcement of the KL-divergence
constraint being closer to |
alpha0 |
numeric, initial guess for |
beta0 |
numeric, initial guess for |
debug |
Logical, whether to output debugging information while running |
Q
is an informed candidate model that uses informative_masking_by_rank
to assign higher probabilities to components that failed earlier (which is
something we typically only know in, say, a simulation study).
The probabilities Q
have two constraints on them. Let
P = (p, ..., p, 1, p, ..., p)
be the bernoulli candidate model that
satisfies conditions C1, C2, and C3. Then, the KL-divergence between P
and Q
is as close as possible to d
while satisfying sum(P) == sum(Q)
.
For d = 0
, Q == P
. As d
increases, Q
becomes more informative about
the components. Given the structure of informative_masking_by_rank
, it may
not be possible to satisfy every d
specified, but we get as close as
we can, which should permit useful experiments.
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