Add renormalized laplace and gaussian distribution and kernels #162
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horizon-blue
approved these changes
Sep 11, 2024
src/b3d/modeling_utils.py
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| p_below_low = tfp.distributions.Laplace(loc, scale).cdf(low) | ||
| p_below_high = tfp.distributions.Laplace(loc, scale).cdf(high) | ||
| log_integral_of_laplace_pdf_over_this_range = jnp.log( | ||
| p_below_high - p_below_low | ||
| ) |
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For numerical stability, perhaps we can consider using Laplace.log_cdf and Laplace. log_survival_function?
(btw also just for clarity: I think log_integral_of_laplace_pdf_over_this_range is actually referring to the integral of laplace within the range?)
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@horizon-blue can you please suggest the right code snippet we need to do log_cdf and log_survival_function here?
| def sample(self, key, latent_color, color_scale, *args, **kwargs): | ||
| return jax.vmap( | ||
| genjax.truncated_normal.sample, in_axes=(0, 0, None, None, None) | ||
| )(split(key, latent_color.shape[0]), latent_color, color_scale, 0.0, 1.0) |
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How to you feel about usingCOLOR_MIN_VAL and COLOR_MAX_VAL instead of hard-coding the magic constant?
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@horizon-blue I made the suggested changes that I knew how to make! |
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