Record · danmackinlay.name

Learning graphical models from data – The Dan MacKinlay stable of variably-well-consider’d enterprises

Dan MacKinlay · 1 October 2026 · 16 min read

A post on danmackinlay.name, published 1 October 2026, has not been cited by any blog yet (corpus.blog, measured 12 October 2026).

A record is what was published and who pointed at it. The text of the post is not shown here: read it at the source, or at its Wayback capture.

Read at danmackinlay.name Wayback capture

0blogs citing

Citing blogs and links by period
Period	Blogs	Links
All time	0	0
Last 90 days	0	0
Last 30 days	0	0
Last 7 days	0	0

Figures for this page
Words blogs link it with	0
External links	78
Length	3,478

Cited by

No blog we hold has cited this post yet.

Links from this post

Link

Host

https://stat.ethz.ch/~mmarloes/?ref=danmackinlay.name

stat.ethz.ch

Learning DAGs with Continuous Optimization Read at blog.ml.cmu.edu

blog.ml.cmu.edu

https://github.com/xunzheng/notears?ref=danmackinlay.name

github.com

https://github.com/larslorch/dibs?ref=danmackinlay.name

github.com

https://github.com/google/jax Who cites this page

github.com

https://differentiable-bayesian-structure-learning.readthedocs.io/?ref=danmackinlay.name

differentiable-bayesian-structure-learning.readthedocs.io

http://www.bnlearn.com/?ref=danmackinlay.name

bnlearn.com

https://github.com/itsrainingdata/sparsebn?ref=danmackinlay.name

github.com

https://github.com/quantumblacklabs/causalnex?ref=danmackinlay.name

github.com

https://github.com/MLResearchAtOSRAM/cause2e?ref=danmackinlay.name

github.com

https://github.com/bd2kccd/py-causal?ref=danmackinlay.name

github.com

https://microsoft.github.io/dowhy/?ref=danmackinlay.name

microsoft.github.io

https://www.ccd.pitt.edu/tools/?ref=danmackinlay.name

ccd.pitt.edu

https://github.com/cmu-phil/tetrad?ref=danmackinlay.name

github.com

https://rawgit.com/cmu-phil/tetrad/development/tetrad-gui/src/main/resources/resources/javahelp/manual/tetrad_tutorial.html?ref=danmackinlay.name

rawgit.com

https://bd2kccd.github.io/docs/r-causal/?ref=danmackinlay.name

bd2kccd.github.io

https://github.com/skggm/skggm?ref=danmackinlay.name

github.com

https://skggm.github.io/skggm/tour?ref=danmackinlay.name

skggm.github.io

http://www.jmlr.org/papers/volume15/hsieh14a/hsieh14a.pdf

jmlr.org

https://causeme.uv.es/?ref=danmackinlay.name

causeme.uv.es

https://github.com/FenTechSolutions/CausalDiscoveryToolbox?ref=danmackinlay.name

github.com

https://github.com/microsoft/csuite?tab=readme-ov-file&ref=danmackinlay.name

github.com

https://arxiv.org/abs/1910.12327 Who cites this page

arxiv.org

https://doi.org/10.1109/TIT.2011.2174612

doi.org

http://arxiv.org/abs/1812.03253

arxiv.org

https://doi.org/10.1109/69.494161

doi.org

https://doi.org/10.1073/pnas.1703764114

doi.org

https://doi.org/10.1146/annurev-statistics-060116-053803

doi.org

http://papers.nips.cc/paper/4087-extended-bayesian-information-criteria-for-gaussian-graphical-models.pdf

papers.nips.cc

https://doi.org/10.1146/annurev-statistics-030718-105312

doi.org

https://doi.org/10.3389/fgene.2019.00524

doi.org

https://doi.org/10.1214/20-AOS2021

doi.org

http://papers.nips.cc/paper/4010-learning-efficient-markov-networks

papers.nips.cc

https://doi.org/10.52202/079017-4044

doi.org

https://doi.org/10.1145/2783258.2783313

doi.org

http://jmlr.org/papers/v14/harris13a.html

jmlr.org

https://doi.org/10.1146/annurev-statistics-031017-100630

doi.org

http://www.cs.toronto.edu/~osindero/PUBLICATIONS/HintonOsinderoBao05_CLMRF.pdf

cs.toronto.edu

http://jmlr.org/papers/v21/19-232.html

jmlr.org

https://doi.org/10.1016/j.artint.2012.01.002

doi.org

http://arxiv.org/abs/1910.08892

arxiv.org

http://arxiv.org/abs/1704.02107

arxiv.org

http://proceedings.mlr.press/v70/kocaoglu17a.html

proceedings.mlr.press

http://arxiv.org/abs/1609.05551

arxiv.org

https://doi.org/10.1609/aaai.v39i17.33961

doi.org

http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_849.pdf

machinelearning.wustl.edu

http://arxiv.org/abs/2007.00631

arxiv.org

http://arxiv.org/abs/1206.6488

arxiv.org

http://jmlr.org/papers/v10/liu09a.html

jmlr.org

https://arxiv.org/abs/1608.04471 Who cites this page

arxiv.org

https://arxiv.org/abs/1811.12359 Who cites this page

arxiv.org

http://arxiv.org/abs/2002.02886

arxiv.org

https://openreview.net/forum?id=YqYt54gU-XV

openreview.net

http://arxiv.org/abs/2102.12353

arxiv.org

http://arxiv.org/abs/1206.6852

arxiv.org

https://doi.org/10.1214/12-EJS740

doi.org

http://arxiv.org/abs/1011.4328

arxiv.org

http://arxiv.org/abs/1910.01751

arxiv.org

http://arxiv.org/abs/1811.04376

arxiv.org

https://doi.org/10.3390/make1010019

doi.org

http://arxiv.org/abs/1910.08527

arxiv.org

http://arxiv.org/abs/1911.07420

arxiv.org

http://arxiv.org/abs/1910.10775

arxiv.org

https://openreview.net/forum?id=aXuWowhIYt

openreview.net

http://arxiv.org/abs/1202.3757

arxiv.org

https://doi.org/10.1038/s41467-019-10105-3

doi.org

https://arxiv.org/abs/1911.10500 Who cites this page

arxiv.org

https://doi.org/10.1109/JPROC.2021.3058954

doi.org

http://arxiv.org/abs/1508.00280

arxiv.org

https://proceedings.mlr.press/v238/thompson24a.html

proceedings.mlr.press

https://proceedings.neurips.cc/paper_files/paper/2022/hash/98a5c0470e57d518ade4e56c6ee0b363-Abstract-Conference.html

proceedings.neurips.cc

http://arxiv.org/abs/2109.03795

arxiv.org

https://doi.org/10.1109/CVPR46437.2021.00947

doi.org

https://doi.org/10.48550/ARXIV.2507.00093

doi.org

https://doi.org/10.48550/arXiv.1904.10098

doi.org

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4729207/

ncbi.nlm.nih.gov

https://doi.org/10.48550/arXiv.1803.01422

doi.org

http://proceedings.mlr.press/v108/zheng20a.html

proceedings.mlr.press

Elsewhere on danmackinlay.name (10)

Probabilistic graphical models – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Sparse regression – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Causal inference on DAGs – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Directed graphical models – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Independence, conditional, statistical – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Multiple testing – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Belief propagation and related algorithms – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Orthonormal and unitary matrices – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Learning graphical models from time series – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Variational message-passing algorithms in graphical models – The Dan MacKinlay stable of variably-well-consider’d enterprises Read at danmackinlay.name

danmackinlay.name

Learning graphical models from data – The Dan MacKinlay stable of variably-well-consider’d enterprises

Cited by

Links from this post

Similar posts