Comments (2)
jtimonen
commented on July 23, 2024
I have been able to run lgpr with N = 600 data points in under a day for a fairly simple model. I have put the warning for N >= 300 because after that sampling starts to take really long. So currently this doesn't scale well because the complexity is N^3, and with for example that data of yours with N about 5000 sampling isn't really possible at least on a normal computer. But yours is an interesting use case, and I have been implementing a basis function approximation which should scale much better, so I will comment on this again when I am able to try it on your data.
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YuHsiangLo
commented on July 23, 2024
That'd be great. Much appreciated!
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Related Issues (20)
- interactions for zs() or categ() variables HOT 4
- Need more colors for plot_components HOT 1
- dependent variable hardcoded for plotting model predictions HOT 2
- Tutorial about real data analysis
- Some generated quantities can have zero variance and r_hat is therefore NaN HOT 1
- Better documentation for get_pred.sampled HOT 1
- Numerical stability of negative binomial random numbers HOT 1
- Avoiding large matrices when computing predictive variance HOT 2
- Better error message when x_cont | x_cat is wrong way around in formula HOT 1
- Models using a standard GP with a continuous covariate fails HOT 3
- Possible bug in get_teff_obs() HOT 1
- Supporting spherical coordinates? HOT 1
- speed ups in code HOT 2
- data.table and tibble objects not accepted as data argument HOT 5
- Build failure on macOS in Macports (clang/gcc): error: cannot initialize a member subobject of type 'std::ostream *' with an lvalue of type 'SEXP' (aka 'SEXPREC *') HOT 17
- [macOS] error: 'stan' was not declared in this scope; did you mean 'tan'? HOT 1
- cc1plus.exe: out of memory in installation from source HOT 3
- Easier/faster prior predictive checks HOT 1
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