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Just writing to test the waters of whether you think this would make a good addition to PyMC3: https://github.com/pymc-devs/pymc3. Creating a new sampler is pretty straight-forward and would allow it …
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Sorry, this may be a stupid question, but could this package (mcstate) implement MCMC instead of PMCMC?
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### Current behavior
Currently, the (adaptive) tempered SMC kernel samples (as desired) from `lmbda * loglikelihood + logprior`, where `loglikelihood` and `logprior` are densities provided by t…
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Hi all,
I am building a structural time series for causal analysis.
I would like to enforce the weights in DynamicLinearRegression (estimated by variational inference) to be non-negative due to i…
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Cool tutorials from @pmocz. Not sure how they fit into this project, but thought I'd bookmark them here.
> Hi everyone! 👋 I wanted to share some introductory ~100 line Python code tutorials with th…
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Hi Professor @nchopin and other contributors,
I am currently working on Bayesian inference using Sequential Monte Carlo (SMC) with tempering, specifically focusing on binary parameter spaces. The a…
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Hi, congratulation on an amazing work... I understand having large characters with a number of parses more than 9 can make the factorial huge... do you have any suggestions on how to incorporate large…
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This looks like a very interesting approach for general nonlinear/non-normal time series models, including point processes
(mentions Poisson ARMA type models as special case, but most applications ar…
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**Reviewers:**
Submitting Author: Jouni Helske (@helske)
Other Package Authors: (delete if none) Name (@mvihola)
Repository: https://github.com/helske/bssm
Version submitted: 2.0.0
Submission ty…
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First, do the version that scales O(n) per generation where n is the number of data points (resulting in an O(n^2) Gibbs sweep).