Fixed log_prob for negative correlation in SineBivariateVonMises
#1515
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fehiepsi
reviewed
Dec 17, 2022
numpyro/distributions/directional.py
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| @@ -400,7 +400,7 @@ def norm_const(self): | |||
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| fs = ( | |||
| lbinoms.reshape(-1, 1) | |||
| + 2 * m * jnp.log(corr) | |||
| + m * jnp.log(corr**2) | |||
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Nice catch! I guess you can clip by tiny here in case users set corr=0?
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Sure, that makes sense.
fehiepsi
reviewed
Dec 17, 2022
fehiepsi
previously approved these changes
Dec 17, 2022
numpyro/distributions/directional.py
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| + 2 * m * jnp.log(corr) | ||
| - m * jnp.log(4 * jnp.prod(conc, axis=-1)) | ||
| fs = lbinoms.reshape(-1, 1) + m * ( | ||
| jnp.log(jnp.clip(corr**2, a_min=jnp.finfo(float).tiny)) |
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The float finfo is too small for single precision, how about jnp.finfo(jnp.result_type(float)).tiny?
fehiepsi
approved these changes
Dec 17, 2022
This was referenced Dec 18, 2022
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Removes bias on correlation in
SBvMnormalization and fixesNaNin normalization for negative correlation (corr < 0.).From #1511.