|
- 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，。
- SVI loss looks good, model not converging - Pyro Discussion Forum
Sorry about the multiple posts - Thanks for the response So I agree that the issue is with the likelihood I’ve got: # pyro sample("obs", dist Bernoulli(loc_img) to_event(1), obs=x reshape(-1, self data_dim)) pyro deterministic("obs", loc_img) Bernoulli is supported on [0,1] but my data is [-1,1] My pytorch implementation works directly with the decoded latents, to calculate loss - no
- Batch processing numpyro models using Ray - forum. pyro. ai
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
- Reproducing transformed gamma distribution - Misc. - Pyro Discussion Forum
I’m developing a probabilistic error model in Pyro, where I’m modeling errors as samples from a transformed Gamma distribution My goal is to use MCMC to simulate realistic error values based on this model
- 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
- Error in Gaussian Process Example - Pyro Discussion Forum
Dear all, Have anyone try the gaussian process example in numpyro documentation? https: num pyro ai en stable examples gp html Unfortunately I got this error when I
- Help post，shape problem - Pyro Discussion Forum
with pyro plate("data", x size(0)): pyro sample("obs", dist Normal(mu, sigma), obs=y squeeze(-1),infer={"scale": annealing_factor}) #obs，真实数据+噪声
- Custom distribution for mixture model - Pyro Discussion Forum
I think I am doing the log_prob calculation correctly as the two methods produce the same values for the same data, but when I try and fit the model using MCMC I don’t get anything like sensible results The traces of cluster_proba jump back and forth between 0 and 1 during sampling when the clusters are about equally probable This works with minor modifications (priors from a Beta
|
|
|