# Local BPS (Chain of Gaussians)

(*the code for this example can be found here, note that the doc rendered here was automatically generated, if you want to fix it, please do it in the julia code directly*)

The approach to using the local BPS is much the same as for the global one except that you need to specify a `FactorGraph`

. That object will contain the structure of the factor graph (i.e.: which factor is connected to which variables) as well as the list of all factors (which have a `lgradll`

and `nextevent`

since each factor can be seen individually as a small BPS).

Below, we show how to declare a chain of bivariate gaussians:

```
using PDSampler
nfac = 3 # number of factors
mvg = MvGaussianStandard(zeros(2),eye(2))
# all factors have that same likelihood
chainfactor(i) = Factor(
(x,v)->nextevent_bps(mvg, x, v),
(x)->gradloglik(mvg, x),
i )
# assemble into a chain graph
chain = chaingraph([chainfactor(i) for i in 1:nfac])
```

This is a simple graph with a known structure so that it's already defined through the `chaingraph`

function (in `src/local/factorgraph.jl`

). For an arbitrary graph, you would need to provide two things:

the structure of the factor graph: a list of list where each element corresponds to a factor and the corresponding list contains the indices of the variables attached to that factor

the list of factors

The rest is very similar to the global BPS:

```
srand(123)
lambdaref = .01
maxnevents = 10000
T = Inf
nvars = chain.structure.nvars
x0 = randn(nvars)
v0 = randn(nvars)
v0 /= norm(v0)
lsim = LocalSimulation(chain, x0, v0, T, maxnevents, lambdaref)
(all_evlist, details) = simulate(lsim)
```

The `all_evlist`

object contains a list of `EventList`

corresponding to what happened on each of the factors. It can also be sampled using `samplelocalpath`

(cf. `src/local/event.jl`

).