Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Extend the top end of the adaptive time grid to include large values #7

Open
hyanwong opened this issue Sep 5, 2019 · 1 comment · May be fixed by #233
Open

Extend the top end of the adaptive time grid to include large values #7

hyanwong opened this issue Sep 5, 2019 · 1 comment · May be fixed by #233
Labels
invalid This doesn't seem right

Comments

@hyanwong
Copy link
Member

hyanwong commented Sep 5, 2019

At the moment we do not include the quantile=1 upper value, as (for the beta distribution) this is infinite. I think this might explain why we often truncate the oldest MRCAs to be too recent. There should be a relatively easy way to extend the time grid to incorporate a higher upper bound (probably something to ask Gil about)

@hyanwong hyanwong changed the title Extend the top end of the adaptive grid to include large values Extend the top end of the adaptive time grid to include large values Sep 5, 2019
@awohns awohns added this to the Prerelease tsdate milestone Nov 1, 2019
@awohns awohns added the invalid This doesn't seem right label Nov 21, 2020
hyanwong added a commit to hyanwong/tsdate that referenced this issue Jan 9, 2023
Finally fixes tskit-dev#7 and produces a much nicer looking fit for large tree sequences, but leads to slightly worse performance for tiny tree sequences such as those tested in test_accuracy.py, because of tskit-dev#230. When we fix that, this PR should provide uniformly better performance, I hope
@hyanwong hyanwong linked a pull request Jan 9, 2023 that will close this issue
hyanwong added a commit to hyanwong/tsdate that referenced this issue Mar 21, 2023
Finally fixes tskit-dev#7 and produces a much nicer looking fit for large tree sequences, but leads to slightly worse performance for tiny tree sequences such as those tested in test_accuracy.py, because of tskit-dev#230. When we fix that, this PR should provide uniformly better performance, I hope
hyanwong added a commit to hyanwong/tsdate that referenced this issue Jun 14, 2023
Finally fixes tskit-dev#7 and produces a much nicer looking fit for large tree sequences, but leads to slightly worse performance for tiny tree sequences such as those tested in test_accuracy.py, because of tskit-dev#230. When we fix that, this PR should provide uniformly better performance, I hope
@hyanwong
Copy link
Member Author

Simple fix is in #233

hyanwong added a commit to hyanwong/tsdate that referenced this issue Jul 13, 2023
Finally fixes tskit-dev#7 and produces a much nicer looking fit for large tree sequences, but leads to slightly worse performance for tiny tree sequences such as those tested in test_accuracy.py, because of tskit-dev#230. When we fix that, this PR should provide uniformly better performance, I hope
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
invalid This doesn't seem right
Projects
None yet
Development

Successfully merging a pull request may close this issue.

2 participants