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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 am not familiar enough with the details of your model to fix the code for you, but this is covered in detail in the tutorials i linked to, especially the bayesian regression with svi section of the bayesian regression tutorial. I want to generalize this into a chinese restaurant process involving an “infinite” number of states
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Beyond pyro’s tutorials, a popular community resource for getting started with bayesian data science is the book “statistical rethinking”, for which all code snippets have been ported to pyro and numpyro by @fehiepsi and others. Pyro.sample(y, y_dist, obs=y) # or pyro.factor(y_loss, y_loss_tensor) return Hello, first off, amazing job on pyro
At the moment, i sample a guide trace for each desired posterior predictive sample, replay the model with the guide trace, and sample once from it, like this
Ppc = [] dummy_obs = torch.zeros((1,self.d)) for sample in range(n_samples) There are a couple of similar posts here about this, more so in this thread but i don’t think i have a clear understanding of the solution / proper use of init_to_value I aim to initialize all sites in my model with the output of svi map result (a dictionary which the keys are the site names and the values are the result of the optimization) From my inspection of the init_to_value function.
Hi everyone, i’m quite new on pyro framework I’m trying to understand its functionalities building some basic bayesian neural networks I successfully built a basic classifier for the mnist dataset, writing the model … Here’s some code i’ve written, but i’m struggling to make sense of how to fix it
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There’s some observed data from two countries, from two cities each
So i’d like to make a hierarchical model with two levels Each city can have its own mean and std each city within the same country shares hyperpriors each country shares global hyperpriors here’s a complete reproducible example. >>> pyro.__version__ '0.3.3+0113e0b3' and from pyro.infer.mcmc.api import mcmc, nuts it may be helpful to know that is only crashes when the number of steps gets large, e.g I can do 50 steps with no problem, but 1000 warmup steps leads to crashing
I also tried running it in a script (so not in a notebook) and the problem remains
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