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Hello there!
I noticed that TFP still lacks an implementation of SGHMC [1]. I would like to use it in some of my projects, and since I am already relying heavily on the TFP ecosystem, it would be v…
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**What is the main topic of this tutorial**: Explain what are MCMC, Metropolis-Hastings, Hamiltonian Monte Carlo, and how to use them in practice.
**One line description**
This tutorial would help…
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@stanniszhou gave a talk by this title at PROBPROG 2020. Could AdvancedHMC be adapted to do this? HMC for discrete variables could be a game changer :)
https://stanniszhou.github.io/papers/mixed_hm…
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# Context
Documenting field-level explicit likelihood inference from a differentiable cosmological model.
In [code](https://github.com/hsimonfroy/montecosmo/blob/a7346788b5555b2f6b14bff3e9dc2f4c9…
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Suggestion: add a 3rd example to [tfp.mcmc.HamiltonianMonteCarlo](https://www.tensorflow.org/probability/api_docs/python/tfp/mcmc/HamiltonianMonteCarlo) showing how to infer the posterior parameters o…
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My entries in particular all over the shop with this!
What style should we use? Harvard? For clarity this means of the below form,
Girolami, M. and Calderhead, B., 2011. Riemann manifold langevi…
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I run Hamiltonian Monte Carlo on 4 copies of my model for 10^5 steps on a GPU.
Each copy of the model contains about 1000 parameters. The log-likelihood function contains `tf.scan`. The main (cpu) m…
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The file `python/progressive_turnaround.py` contains an implementation of progressive sampling a proposal point during trajectory exploration, thus reducing the number of leapfrog steps necessary to e…
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#### Summary:
It would be nice to have versions of HMC/NUTS that support a low-rank plus diagonal metric a la L-BFGS.
This will require the following.
1. `low_rank_e_metric` and `low_rank_e…
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When derivatives of the objective function are available, MCMC methods that use them, for example Hamiltonian Monte Carlo, can be very much faster than MCMC methods that aren't. PINT has gone to great…