Hi all,
I've been trying to learn more about the Bayesian inference techniques
introduced in
https://journals.aps.org/prl/pdf/10.1103/PhysRevLett.123.128301 and have
been having much joy trying the graph-tool software - it is a fantastic
tool. I have been having difficulty understanding how one would estimate
both the posterior distribution of a graph and the corresponding
transmission probabilities in the SI dynamics example as suggested in the
Twitter example of the aforementioned paper. For example taking the example
from the cookbook (which is very clear) regarding these ideas but now
assigning a edge property for the transmission probabilities (although
leaving them homogeneous across all edges)
#####################################
### Look at using an edge based beta value
# We will first simulate the dynamics with a given network
g = gt.collection.data["dolphins"]
tau = g.new_ep("double", .7) # transmission probabilities
### simulate the dynamics
ss = []
for i in range(100):
si_state = gt.SIState(g, beta=tau)
s = [si_state.get_state().copy()]
for j in range(10):
si_state.iterate_sync()
s.append(si_state.get_state().copy())
# Each time series should be represented as a single vector-valued
# vertex property map with the states for each node at each time.
s = gt.group_vector_property(s)
ss.append(s)
####################################
and now considering the inference part my confusion is arising from how to
assign a prior transmission value as a edge property when we are
initializing the graph as empty. So below I am just changing the function
collect_marginals as I want the local transmission probs (accessed through
get_x() ) rather than the global beta as before and also not using a global
beta in the block state. I assume here that if we were only interested in
the transmisison probs i.e., not the graph posterior, then we'd assign an
edge property to the graph but if we assume that we also don't know the
edges I don't see how to assign the prior distribution. (I don't think beta
= .7 is setting the prior to 0.7 beforehand but I may be mistaken here?)
####################################
# Prepare the initial state of the reconstruction as an empty graph
u = g.copy()
u.clear_edges()
ss = [u.own_property(s) for s in ss] # time series properties need to be
'owned' by graph u
def collect_marginals(s):
global gm, bs
gm = s.collect_marginal(gm)
bs.append(s.bstate.b.a.copy())
betas.append(s.get_x().copy())
# Create reconstruction state
# but don't use a global beta and also don't state the no. of edges
# note the prior beta is the same as the true one?
rstate = gt.EpidemicsBlockState(u, s=ss, beta=.7, r=1e-6,
nested=False)
# Now we collect the marginals for exactly 100,000 sweeps, at
# intervals of 10 sweeps:
gm = None
bs = []
betas = []
gt.mcmc_equilibrate(rstate, force_niter=10000, mcmc_args=dict(niter=10,
xstep=0),
callback=collect_marginals)
##################################
but when I access the arrays within betas after they all just consist of 0
values (through betas[i].get_array()) which makes me think I am not doing
this process correctly (the graph similarities with the posterior and actual
are also negligible~0.05).
Any help with this problem (I may be doing something very incorrect in the
process) and how to correctly implement an edge prior without knowledge of
the graph would be greatly appreciated as I would like to delve further into
these techniques.
--
Sent from: https://nabble.skewed.de/
_______________________________________________
graph-tool mailing list
[email protected]
https://lists.skewed.de/mailman/listinfo/graph-tool
