https://github.com/red-portal/hierarchicalbayeszoo.jl
https://github.com/red-portal/hierarchicalbayeszoo.jl
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/red-portal/hierarchicalbayeszoo.jl
- Owner: Red-Portal
- Created: 2023-08-15T20:33:06.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-23T19:57:32.000Z (over 1 year ago)
- Last Synced: 2025-01-18T03:45:58.958Z (5 months ago)
- Language: Julia
- Size: 135 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# HierarchicalBayesZoo
This code base is using the [Julia Language](https://julialang.org/) and
[DrWatson](https://juliadynamics.github.io/DrWatson.jl/stable/)
to make a reproducible scientific project named
> HierarchicalBayesZoo
To (locally) reproduce this project, do the following:
0. Download this code base. Notice that raw data are typically not included in the
git-history and may need to be downloaded independently.
1. Open a Julia console and do:
```
julia> using Pkg
julia> Pkg.add("DrWatson") # install globally, for using `quickactivate`
julia> Pkg.activate("path/to/this/project")
julia> Pkg.instantiate()
```
This will install all necessary packages for you to be able to run the scripts and
everything should work out of the box, including correctly finding local paths.
You may notice that most scripts start with the commands:
```julia
using DrWatson
@quickactivate "HierarchicalBayesZoo"
```
which auto-activate the project and enable local path handling from DrWatson.
## Todo List
- [ ] Deep exponential families (Ranganath, *et al.*, 2015)
- [x] Dirichlet-exponential non-negative matrix factorization (Kucukelbir, *et al.*, 2017)
- [ ] Gamma-gamma non-negative matrix factorization (Canny, 2004; Gopalan, *et al.*, 2015)
- [ ] Probabilistic principle component analysis (Tipping & Bishop, 1999)
- [ ] Non-conjugate Gaussian-Bernoulli matrix factorization (Agrawal, *et al.*, 2021)
- [ ] Difference correlated random walk model (Jonsen, *et al.*, 2013; Auger‐Méthé, *et al.*, 2021)
- [ ] Stochastic volatility
- [ ] Bayesian regression with measurement error?
## References
- Agrawal, A., & Domke, J. (2021). Amortized variational inference for simple hierarchical models. Advances in Neural Information Processing Systems, 34, 21388-21399.
- Canny, J. (2004, July). GaP: a factor model for discrete data. In Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 122-129).
- Gopalan, P., Hofman, J. M., & Blei, D. M. (2015, July). Scalable Recommendation with Hierarchical Poisson Factorization. In UAI (pp. 326-335).
- Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., & Blei, D. M. (2017). Automatic differentiation variational inference. Journal of machine learning research.
- Ranganath, R., Tang, L., Charlin, L., & Blei, D. (2015, February). Deep exponential families. In Artificial Intelligence and Statistics (pp. 762-771). PMLR.
- Tipping, M. E., & Bishop, C. M. (1999). Probabilistic principal component analysis. Journal of the Royal Statistical Society Series B: Statistical Methodology, 61(3), 611-622.
- Jonsen, I. D., Basson, M., Bestley, S., Bravington, M. V., Patterson, T. A., Pedersen, M. W., ... & Wotherspoon, S. J. (2013). State-space models for bio-loggers: A methodological road map. Deep Sea Research Part II: Topical Studies in Oceanography, 88, 34-46.
- Auger‐Méthé, Marie, Ken Newman, Diana Cole, Fanny Empacher, Rowenna Gryba, Aaron A. King, Vianey Leos‐Barajas et al. "A guide to state–space modeling of ecological time series." Ecological Monographs 91, no. 4 (2021): e01470.