Implement `jax.scipy.stats.bootstrap`
See original GitHub issueI think it would be super neat to have a JAX version of the scipy.stats.bootstrap
function: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.bootstrap.html
Issue Analytics
- State:
- Created a year ago
- Comments:8 (3 by maintainers)
Top Results From Across the Web
jax.scipy package - JAX documentation - Read the Docs
Compute the Cholesky decomposition of a matrix, to use in cho_solve. cho_solve (c_and_lower, b[, overwrite_b, ...]) Solve the linear equations A x =...
Read more >scipy.stats.bootstrap — SciPy v1.9.3 Manual
Resample the data: for each sample in data and for each of n_resamples, take a random sample of the original sample (with replacement)...
Read more >Trying to understand Bootstrapping w/ Python - Cross Validated
When you calculate your lower and upper bounds, you are dividing your standard deviations of your means by √1000, which I assume you...
Read more >Visualizing the Bivariate Gaussian Distribution in Python
Returns: A multivariate normal random variable object scipy.stats. ... Below is the implementation of the covariance matrix:.
Read more >scipy.stats.bootstrap — SciPy v1.11.0.dev0+1138.4e738d7 ...
scipy.stats.bootstrap# ... Compute a two-sided bootstrap confidence interval of a statistic. When method is 'percentile' , a bootstrap confidence interval is ...
Read more >
Top Related Medium Post
No results found
Top Related StackOverflow Question
No results found
Troubleshoot Live Code
Lightrun enables developers to add logs, metrics and snapshots to live code - no restarts or redeploys required.
Start Free
Top Related Reddit Thread
No results found
Top Related Hackernoon Post
No results found
Top Related Tweet
No results found
Top Related Dev.to Post
No results found
Top Related Hashnode Post
No results found
Hi - that sounds like a good plan for implementation. Regarding
BootstrapResult
– I think it’s just a simple dataclass or named tuple, you could create a similar container for the results in a JAX implementation, I’d lean toward NamedTuple because you get pytree flattening for free.Regarding the return shape: it looks like it only depends on static properties of the inputs, so it should be fine for JAX.
Any update on this?