Comments (3)
#30 makes MultivariateNormal take matrix square-roots instead of Cholesky factors.
from tornadox.
Notably, the part of propagate_cholesky_factors
that makes sure all the diagonal elements are positive can be removed if only square root matrices are used.
This would be useful, because making sure that the sign(0)=0 behaviour does not mess up updates makes it almost impossible to jit/grad/vmap the propagation functions
from tornadox.
I consider this solved, because sqrts are used everywhere now isntead of proper cholesky factors.
from tornadox.
Related Issues (20)
- Krylov methods in truncated EK1 HOT 1
- Inconsistent `__init__` in EK1 and ODEFilter
- More efficient projection matrices
- Representation of ODEFilter means? HOT 2
- RK init HOT 3
- Diagonal EK1 Todo HOT 2
- Reference means as matrix HOT 4
- Solve interface
- Diagonal Jacobians (and more?)
- ODE dimension init
- First step
- RK init + Radau should get the jacobian
- Package name HOT 1
- Consider replacing dataclasses with namedtuples
- Migrate the EK0 and DiagonalEK1 to a diagonal diffusion model HOT 3
- Stats
- Lorenz96 is not working HOT 4
- How to save covariances for very high-dimensional problems? HOT 2
- More efficient diagonal Jacobians for the PDEs HOT 3
- Citation
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from tornadox.