Filzbach is a flexible, fast, robust, parameter estimation engine that allows you to parameterize arbitrary, non-linear models, of the kind that are necessary in biological sciences, against multiple, heterogeneous data sets. Filzbach allows for Bayesian parameter estimation, maximum likelihood analysis, priors, latents, hierarchies, error propagation and model selection, from just a few lines of code.
Traditionally, ecology and biology has been largely split into the purely empirical (generating or analysing data with only informal use of models) and the purely theoretical (analysing models that have been, at best, only informally constrained with data). However, to create a precise, predictive, understanding of ecological and biological systems it is necessary to bridge this gap, using data to formally parameterize, and select between, competing models.
Get a feel for Filzbach by trying our web sampler which features the following pre-defined illustrative models:
To code up and run your own Filzbach analysis in C++, C# or another programming language download the Filzbach package which includes all necessary libraries, many examples, and a user guide.
Many collaborators and students have used Filzbach, provided helpful feedback and reported issues. Andreas Heil worked on the Filzbach probability distributions of the implementation of parallel chains.