Saving the World with Bayesian Modeling

• BayesianEcosystems_IAP

  2 10 0.0 Jupyter Notebook

  Notes and code for Bayesian ecosystem modeling IAP course

I am far from an expert on the pros/cons between the two, but with Stan I can do systems of differential equations a lot easier. In some cases, it is infinitely easier. There are some really good examples for Stan on this, and the language structure just handles this easier: https://github.com/gregbritten/BayesianEcosystems_IAP/blob/m...

Julia also does better with differential equations in MCMC in general, in my experience compared to pymc3, but it can still be problematic. In some cases, it is most ideal to use the wrapper DiffEqBayes, but it has been somewhat abandoned in support for a neural differential equations package, DiffEqFlux. In some cases if you have a lot of data input, it simply is not possible to use DiffEqBayes. Stan allows for a lot of data input too alongside the system of differential equations, without any issue at all.

Not only that, Statistical Rethinking is based on Stan, first and foremost. Everything else (and there are a lot of renditions in other MCMC packages) is reprogrammed and reformatted to perform the same tasks as Stan does, and in a lot of cases, it just is not as nice.

• numpyro

  2 2,039 8.7 Python

  Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.

  

Perhaps an alternative to look into: Numpyro [1] has a JAX backend so can be really fast when compiled; and it can run on GPUs. So that might be helpful for your problem with loads of data.

[1] https://github.com/pyro-ppl/numpyro

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

Suggest a related project