pgjax

On-device Pólya-Gamma sampling for JAX, as an XLA FFI custom call.

pgjax.pg_sample(h, z, key) draws omega ~ PG(h, z) from inside a JIT'd / lax.scan-ed / pmap-ed program with no host round-trip — the native replacement for jax.pure_callback(random_polyagamma, ...), which pays a device↔host round-trip on every Pólya-Gamma Gibbs sweep and serializes under jax.pmap.

import jax, pgjax
jax.config.update("jax_enable_x64", True)

@jax.jit
def omega_update(h, z, key):
    return pgjax.pg_sample(h, z, key)

Method

pg_sample follows the hybrid dispatch of zoj613/polyagamma (method=None), changed where that scheme is measurably biased:

h > 50: a normal with the exact Pólya-Gamma mean and variance.
8 <= h <= 50: the saddlepoint rejection sampler (Windle, Polson & Scott 2014, arXiv:1405.0506), O(1) per accepted draw.
h = 1 (Bernoulli/logit), or integer h <= 4 with |z| <= 1: the exact Devroye sampler for PG(1, z) (Polson, Scott & Windle 2013), summed h times.
Otherwise, 1 < h < 8 — e.g. Negative-Binomial h = y + alpha: the alternate sampler (Windle, Polson & Scott 2014, Section 4).
h < 1: the Gamma-series representation — 20 terms plus a remainder matched in mean and variance — exact in both moments.

Each departure from polyagamma 2.0.2 removes a bias measured with 2M draws against the exact mean and variance:

Alternate sampler, 1 < h < 4, small |z|: polyagamma runs +0.4–0.9% in the mean and +1–5% in the variance. Its right-hand envelope uses the constant (π/2)^(h/2), where both its Gamma proposal and the density's tail carry (π/2)^h; pgjax uses the latter.
h < 1: the alternate method assumes h >= 1; below it polyagamma runs −0.4–0.8% in the mean. pgjax uses the Gamma series.
4 < h < 8 with |z| <= 4: polyagamma uses the saddlepoint approximation, 1–2% low in the variance. pgjax uses the exact alternate sampler.

The saddlepoint and alternate samplers are ported from polyagamma (BSD-3-Clause). Every regime is tested against the closed-form mean and variance.

The RNG is a per-call xoshiro256** seeded (via splitmix64) from the JAX PRNG key, so draws are reproducible and each call owns its stream — safe when several threads call at once. Normals and exponentials come from ziggurat samplers.

An array longer than 65,536 elements is split into blocks of that size, each with its own stream, and the blocks are drawn in parallel on every hardware thread (set PGJAX_NUM_THREADS to cap it). The blocks depend only on the array length, so a draw is the same on any machine and any thread count. On a large Negative-Binomial model (8.1M cells) this takes the draw from 3.6 s to 0.33 s on 16 threads.

Status

✅ All h > 0: the logit (h = 1), Negative-Binomial (h = y + alpha) and large-shape regimes.
Dispatch is automatic: the sampler is selected from (h, z) with no API change.

CPU-only (the sampler is cheap scalar work; the point is to keep it on-device next to the rest of a JIT'd Gibbs sweep).

Metadata

Release files for pgjax 0.2.0

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Source distribution (sdist)

Source distribution for pgjax 0.2.0
File	Size	Uploaded
pgjax-0.2.0.tar.gz	30.2 kB	Sep 30, 2026	Details

Built distributions (wheels)

Table of built distributions (wheels) for pgjax 0.2.0
File	Interpreter	ABI	Platform	Reset
pgjax-0.2.0-cp314-cp314-win_amd64.whl 304.3 kB Sep 30, 2026	CPython 3.14	CPython 3.14	Windows x86-64	Details
pgjax-0.2.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 95.5 kB Sep 30, 2026	CPython 3.14	CPython 3.14	Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64	Details
pgjax-0.2.0-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl 83.6 kB Sep 30, 2026	CPython 3.14	CPython 3.14	Linux glibc 2.26+ ARM64, Linux glibc 2.28+ ARM64	Details
pgjax-0.2.0-cp314-cp314-macosx_15_0_arm64.whl 63.2 kB Sep 30, 2026	CPython 3.14	CPython 3.14	macOS 15.0+ ARM64	Details
pgjax-0.2.0-cp313-cp313-win_amd64.whl 294.3 kB Sep 30, 2026	CPython 3.13	CPython 3.13	Windows x86-64	Details
pgjax-0.2.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 95.5 kB Sep 30, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64	Details
pgjax-0.2.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl 83.5 kB Sep 30, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.26+ ARM64, Linux glibc 2.28+ ARM64	Details
pgjax-0.2.0-cp313-cp313-macosx_15_0_arm64.whl 63.2 kB Sep 30, 2026	CPython 3.13	CPython 3.13	macOS 15.0+ ARM64	Details
pgjax-0.2.0-cp312-cp312-win_amd64.whl 294.3 kB Sep 30, 2026	CPython 3.12	CPython 3.12	Windows x86-64	Details
pgjax-0.2.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 95.6 kB Sep 30, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64	Details
pgjax-0.2.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl 83.6 kB Sep 30, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.28+ ARM64, Linux glibc 2.26+ ARM64	Details
pgjax-0.2.0-cp312-cp312-macosx_15_0_arm64.whl 63.2 kB Sep 30, 2026	CPython 3.12	CPython 3.12	macOS 15.0+ ARM64	Details
pgjax-0.2.0-cp311-cp311-win_amd64.whl 294.7 kB Sep 30, 2026	CPython 3.11	CPython 3.11	Windows x86-64	Details
pgjax-0.2.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 96.0 kB Sep 30, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64	Details
pgjax-0.2.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl 84.5 kB Sep 30, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.26+ ARM64, Linux glibc 2.28+ ARM64	Details
pgjax-0.2.0-cp311-cp311-macosx_15_0_arm64.whl 64.0 kB Sep 30, 2026	CPython 3.11	CPython 3.11	macOS 15.0+ ARM64	Details

Total release size: 2.2 MB

Release files / pgjax-0.2.0.tar.gz

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Release files / pgjax-0.2.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl

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Upload date	Sep 30, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

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Publishing platform: GitHub Actions
Publishing repository: github.com/knaaptime/pgjax
Publishing commit: github.com/knaaptime/pgjax/tree/02cee73ba8e801ee74cb0c47d66e6862029979f8

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Release files / pgjax-0.2.0-cp312-cp312-macosx_15_0_arm64.whl

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Publishing commit: github.com/knaaptime/pgjax/tree/02cee73ba8e801ee74cb0c47d66e6862029979f8

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Release files / pgjax-0.2.0-cp311-cp311-win_amd64.whl

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Publishing commit: github.com/knaaptime/pgjax/tree/02cee73ba8e801ee74cb0c47d66e6862029979f8

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Release files / pgjax-0.2.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

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Uploaded via	`twine/7.0.0 CPython/3.13.14`

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Publishing repository: github.com/knaaptime/pgjax
Publishing commit: github.com/knaaptime/pgjax/tree/02cee73ba8e801ee74cb0c47d66e6862029979f8

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Release files / pgjax-0.2.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl

Download URL	pgjax-0.2.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
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Upload date	Sep 30, 2026
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Uploaded via	`twine/7.0.0 CPython/3.13.14`

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Publishing platform: GitHub Actions
Publishing repository: github.com/knaaptime/pgjax
Publishing commit: github.com/knaaptime/pgjax/tree/02cee73ba8e801ee74cb0c47d66e6862029979f8

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Release files / pgjax-0.2.0-cp311-cp311-macosx_15_0_arm64.whl

Download URL	pgjax-0.2.0-cp311-cp311-macosx_15_0_arm64.whl
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Upload date	Sep 30, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

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Publishing platform: GitHub Actions
Publishing repository: github.com/knaaptime/pgjax
Publishing commit: github.com/knaaptime/pgjax/tree/02cee73ba8e801ee74cb0c47d66e6862029979f8

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Publishing logs: github.com/knaaptime/pgjax/actions/runs/36767130517/attempts/1

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SHA-256 checksum: a91583bdc787aa5d99fdba07d765eb6f4c271a93fdf949d82d4d95024ed7b3f7 Verifying attestations

Release history Release notifications | RSS feed

This release

0.2.0 This release

Sep 30, 2026 17 release files

0.1.0

Jul 30, 2026 13 release files

pgjax 0.2.0

pgjax

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