Pingala is a Python based machine learning software library that features Bayesian inference for deep learning and geoscientific models via MCMC
It is named after "Pingala" - to pay tribute to ancient Hindu scientist, who invented the binary number system: https://en.wikipedia.org/wiki/Pingala
- MCMC (sequential and parallel implementation)
- Parallel tempering MCMC for statistical models
- Parallel tempering MCMC for neural networks: 1. FNN, 2. RNN, 3. LSTM, 4. GRU
- Parallel tempering MCMC for geoscientific models - Badlands lansacpe evolution models
- Parallel tempering MCMC for geoscientific models - pyReef-Core evolution models
- Surrogate assisted parallel tempering MCMC for neural networks
- Surrogate assited parallel tempering MCMC for geoscientific models - Badlands lansacpe evolution models
The library will combine the Python software frameworks from the following papers:
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Pall J; Chandra R; Azam D; Salles T; Webster JM; Scalzo R; Cripps S, 2020, 'Bayesreef: A Bayesian inference framework for modelling reef growth in response to environmental change and biological dynamics', Environmental Modelling and Software, vol. 125, pp. 104610 - 104610, http://dx.doi.org/10.1016/j.envsoft.2019.104610
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Chandra R; Kapoor A, 2020, 'Bayesian neural multi-source transfer learning', Neurocomputing, vol. 378, pp. 54 - 64, http://dx.doi.org/10.1016/j.neucom.2019.10.042
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Chandra R; Müller RD; Azam D; Deo R; Butterworth N; Salles T; Cripps S, 2019, 'Multicore Parallel Tempering Bayeslands for Basin and Landscape Evolution', Geochemistry, Geophysics, Geosystems, vol. 20, pp. 5082 - 5104, http://dx.doi.org/10.1029/2019GC008465
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Chandra R; Azam D; Müller RD; Salles T; Cripps S, 2019, 'Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands', Computers and Geosciences, vol. 131, pp. 89 - 101, http://dx.doi.org/10.1016/j.cageo.2019.06.012
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Chandra R; Jain K; Deo RV; Cripps S, 2019, 'Langevin-gradient parallel tempering for Bayesian neural learning', Neurocomputing, vol. 359, pp. 315 - 326, http://dx.doi.org/10.1016/j.neucom.2019.05.082
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Chandra R; Azam D; Kapoor A; Müller RD, Surrogate-assisted Bayesian inversion for landscape and basin evolution models, http://dx.doi.org, http://arxiv.org/abs/1812.08655v1
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Chandra R; Jain K; Kapoor A, Surrogate-assisted parallel tempering for Bayesian neural learning, http://dx.doi.org, http://arxiv.org/abs/1811.08687v1