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Thanks for this! Can you remove the changes to the |
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I discarded any changes to the existing distributions |
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Sorry for a broken PR, I believed the tests pass, but apparently I made some mistake when running the tests locally. Would appreciate any help deciphering the error messages, it doesn't seem very understandable what the problem is exactly to me. But apparently it somehow has to do with the distribution being possibly non-differentiable at mu=y. The suspect line is:
In case when mu=y, we get:
which I think should evaluate to 0, and also when beta=1
should also evaluate to 0 I would expect? Is pow(0,0) actually undefined in STAN math? And what would be the recommended technique for special-case handling? |
No worries! It's often quite a miracle when these things pass first try. You can run the exact test that is given issues locally by running
It's defined (#3031), but it's not twice differentiable: #2993 (comment), which I believe is the problem here. This second comment is by @andrjohns who can probably also give you suggestions about what to do in this case. It's possible that what you're doing in the code is fine and you just need to have the test call it somewhere else where it will be better behaved |
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OK, so it may be just a testing problem. But this relates to something else: The case when beta=1 where the tests are apparently breaking is maybe just a tip of the iceberg: when beta<1, I write in the code comments the partials are undefined when y=mu, because we then get |
SteveBronder
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Just some starting Q comments.
I'm rather unfamiliar with this distribution so will need to do some reading before I can comment on any of the math here
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Some notes on the distribution (I made some mistakes in the #3133 comments but here should be corrected): We have the density: The parameters The log-likelihood is then: Wolfram Mathematica outputs the following partials: Using algebraic manipulations I change this to the form: This is where I believe the test errors are comming from when testing the case The this one has a also potential boundary problem when The I tend to believe that @WardBrian is right that this is perhaps a problem with testing the distribution at these boundaries, but idk what a good approach to this is. PDF plot from wikipedia for |
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Incorporated changes requested by @SteveBronder although that doesn't solve the test errors. I think those are more like just performance+stylistic improvements. See the notes and discussion above. |
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Hi, @andrjohns! Could you please go through the above comments and provide me with any feedback on how to properly test this or resolve some other way? |
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pushed some changes which should fix the failing tests. |
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Jenkins Console Log Machine informationDistributor ID: Ubuntu Description: Ubuntu 20.04.3 LTS Release: 20.04 Codename: focal CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian Address sizes: 46 bits physical, 48 bits virtual CPU(s): 80 On-line CPU(s) list: 0-79 Thread(s) per core: 2 Core(s) per socket: 20 Socket(s): 2 NUMA node(s): 2 Vendor ID: GenuineIntel CPU family: 6 Model: 85 Model name: Intel(R) Xeon(R) Gold 6248 CPU @ 2.50GHz Stepping: 7 CPU MHz: 999.793 CPU max MHz: 3900.0000 CPU min MHz: 1000.0000 BogoMIPS: 5000.00 Virtualization: VT-x L1d cache: 1.3 MiB L1i cache: 1.3 MiB L2 cache: 40 MiB L3 cache: 55 MiB NUMA node0 CPU(s): 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78 NUMA node1 CPU(s): 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,73,75,77,79 Vulnerability Gather data sampling: Mitigation; Microcode Vulnerability Indirect target selection: Mitigation; Aligned branch/return thunks Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Old microcode: Not affected Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Mitigation; Enhanced IBRS Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop Vulnerability Srbds: Not affected Vulnerability Tsa: Not affected Vulnerability Tsx async abort: Mitigation; TSX disabled Vulnerability Vmscape: Mitigation; IBPB before exit to userspace Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi pku ospke avx512_vnni md_clear flush_l1d arch_capabilities G++: g++ (Ubuntu 9.4.0-1ubuntu1~20.04) 9.4.0 Copyright (C) 2019 Free Software Foundation, Inc. This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. Clang: clang version 10.0.0-4ubuntu1 Target: x86_64-pc-linux-gnu Thread model: posix InstalledDir: /usr/bin |
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@SteveBronder I did |
Summary
This is my take on the request #3133 I created a while ago.
The Generalized normal distribution a.k.a. exponential power distribution generalizes the normal, Laplace (double exponential) and in limit also uniform distribution. This makes it an interesting prior choice as it generalizes the corresponding MAP regularization coefficient to L_p norm and can also be interesting to model noise in regression problems for basically the same reasons.
This is my first attempt at implementing a distribution to STAN, I tried to follow the guide https://mc-stan.org/math/md_doxygen_2contributor__help__pages_2adding__new__distributions.html but there are likely some problems, so I welcome any comments.
So far I implemented only the lpdf, if other files like lcdf etc are strictly required, please let me know.
Tests
I added one test file analogical to the one for normal distribution.
Side Effects
I included some very minor code polishing to the related normal and double exponential distributions (I used those as a starting point) which shouldn't do anything and can be discarded if undesired.
Release notes
Added generalized_normal_lpdf
Checklist
Copyright holder: Me
The copyright holder is typically you or your assignee, such as a university or company. By submitting this pull request, the copyright holder is agreeing to the license the submitted work under the following licenses:
- Code: BSD 3-clause (https://opensource.org/licenses/BSD-3-Clause)
- Documentation: CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
the basic tests are passing
./runTests.py test/unit)make test-headers)make test-math-dependencies)make doxygen)make cpplint)the code is written in idiomatic C++ and changes are documented in the doxygen
the new changes are tested