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- 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
- 大家一起来完善官方教程汉化版 - Pyro Discussion Forum
要做一些高斯过程相关的研究，刚接触pyro， 浏览了你的Introduction部分，能在翻译原教程的基础上加入概率图和重点提炼等以帮助理解，着实不错，当然这也是我个人觉得汉化教程最应该具有的闪光点，。
- 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
- Site name clash for model with multiple HSGPs - forum. pyro. ai
I’m trying to model the distribution of dust in galaxies At any given point, the distribution is LogNormal, but I’m trying to use a GP prior on the mu, sigma of the underlying Normal distribution to capture the spatial information between different positions Now, since these have very different expected properties, I’m trying to use a different GP for each parameter, but with a shared
- Unexpected (?) Enumerate Error with AutoNormal - numpyro - Pyro . . .
Below is a snippet of some code I have I want to subsample a specific dimension to run SVI with the AutoNormal guide, but different sections of my output have different likelihoods, so I’ve created this nested plate structure with numpyro plate("data", size = self n, subsample_size=100 if inference_method == "svi" else self n, dim=-2) as ind: mu = f( X[ind], ) # tensor of size (K, 100
- Diagnosing convergence for a very simple model - numpyro - Pyro . . .
I’ve been trying to monitor convergence of model parameters using the cosine similarity between changes of parameter values The basic idea is as follows If the variational parameters are very far from the optimum, the optimizer will consistency push the parameters in the right direction and the similarity between changes is large If the variational parameters are close to the optimum
- Where can i show my ideal? - Site Feedback - Pyro Discussion Forum
In the process of using pyro, I have some experience or idea, it may be boring or it may be wrong But if I want to share it as an experience, at least for people to see when they’re bored, where should I post it?
- Pyro Discussion Forum
Forum For Pyro Developers
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