Potential issue with SVI using init_params argument - numpyro - Pyro . . . I’m trying to utilize SVI with the AutoDelta guide I run the optimization for a certain number of steps, and save the final parameters to a dictionary and check if the parameters have converged to my liking If not, when I re-run the optimization (with a reduced learning rate), and pass in the last saved parameters as initial parameters for this new optimization stage, the init_loss printed
Adam optimizer before NUTS? - Pyro Discussion Forum I’m trying to infer the parameters of a non-linear ODE system Would using a gradient descent optimizer like Adam (eg from optax) to initialize the guess starting point for NUTS be useful? Is something like this already implemented in numpyro? I’m finding that the time to convergence for my NUTS inference is very sensitive to how small my uncertainties are that go into my Gaussian
Unexpectedly different outcomes when initializing via NUTS or MCMC I’m finding that setting initial parameter values through NUTS or through MCMC gives different results, even though they should be the same I’ve checked that the parameter values are initialized to be the same from the first sample taken in a run either using nuts_kernel = NUTS(model, dense_mass=dense_mass, max_tree_depth=6, init_strategy=init_to_value sampler = MCMC(nuts_kernel, num
Custom distribution for mixture model - Pyro Discussion Forum You can check how the shapes work at Tensor shapes in Pyro — Pyro Tutorials 1 9 0 documentation d: batch_shape + event_shape value: sample_batch_shape + event_shape d log_prob(value): broadcast_shapes(batch_shape, sample_batch_shape) If you think this is the issue of log_prob, you can check:
Is `callback` supported for numpyro NUTS sampler? - numpyro - Pyro . . . Hi all, I’m using Numpyro as a NUTS sampler in a PyMC model and would like to add a callback to monitor the number of divergences and stop the sampling when it’s greater than X amount of divergences I found this response in the PyMC and want to know if that has changed or if there is a way to do it now This could be only in PyMC but already asked there #7419 🙂 sample_numpyro_nuts does
Batch processing numpyro models using Ray - numpyro - Pyro Discussion Forum Hello again, Related post: Batch processing Pyro models so cc: @fonnesbeck as I think he’ll be interested in batch processing Bayesian models anyway I want to run lots of numpyro models in parallel I created a new post because: this post uses numpyro instead of pyro I’m doing sampling instead of SVI I’m using Ray instead of Dask that post was 2021 I’m running a simple Neal’s funnel
Variational Inference for Dirichlet process clustering - Pyro . . . Hi there! This is my first time using Pyro so I am very excited to see what I can built with it 🙂 Specifically, I am trying to do finite Dirichlet Process clustering with Variational Inference I want to generalize this into a Chinese Restaurant Process involving an “infinite” number of states But for now, I am just generating 1-D data from 3 Gaussians with proportions given by a
Help post，shape problem - Pyro Discussion Forum elbo = TraceMeanField_ELBO(num_particles=10,vectorize_particles=True)，Error vectorize_particles set to False，How to solve this problem Traceback (most recent call
Pyro Discussion Forum The Future of Pyro It’s been almost three years since we released the alpha version of Pyro in November 2017 And what a ride it’s been! We’ve been thrilled to see our user and contributor base continue to grow, with di… 1: 6900: October 15, 2020