Comments (3)
I thought this before, but didn't manage to find a good solution. Another possible use case:
x = Array(8,8)
x[:, 1:4] ~ Normal
from dynamicppl.jl.
Note: using matrix is the vectorisation way actually works around this problem, BUT users need to fetch variables using the nested-array syntax:
@model xxx() = begin
m = Matrix(2,2)
m ~ [MvNormal(...)]
end
chn = sample(...)
chn[Symbol("m[1][1]")] # this works
from dynamicppl.jl.
Fixed by #295
from dynamicppl.jl.
Related Issues (20)
- Remove `NamedDist` in favour of `VarName` interpolation HOT 1
- Simplify `assume`/`observe` design HOT 1
- Error with `.~` and `rand` HOT 2
- Supporting mutating ADs in models that fill arrays of parameters HOT 7
- Roadmap for depreciating `VarInfo` in favour of `SimpleVarInfo` HOT 5
- Type instability: assigning slices in assumptions HOT 2
- "[DynamicPPL] attempt to link a linked vi" warning when aborting sampling and returning minus infinity HOT 6
- Tests fail on Julia 1.8 HOT 2
- Name clash caused by submodels is hard to debug HOT 3
- `TypedVarInfo` failing for certain models over empty vectors HOT 1
- Remove use of `threadid` HOT 9
- Models with dynamic dimensionality
- Possibly confusing `.~` meaning HOT 6
- WARNING: Method definition subsumes [...] overwritten HOT 4
- Support for linking distributions with embedded support HOT 10
- Use merge queue instead of bors? HOT 1
- InferenceObjects integration HOT 12
- Adding StatsBase.predict to the API HOT 7
- `LogDensityFunction`: Temporary variable is captured as a model parameter? HOT 3
- Conditioning with Turing Chains `name_map` HOT 1
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