You can now incorporate insights from literature or lab data directly into your models using Bayesian priors—bringing more accuracy and confidence to your parameter estimation and uncertainty quantification.

• Smarter Modeling: Integrate expert knowledge with ease using Bayesian priors for more informed parameter estimation.

• Flexible Prior Types: Choose from normal or lognormal distributions, available in both univariate and multivariate forms—ideal for handling parameter collinearity.

• Better Inference: Achieve more robust and trustworthy results with enhanced support for uncertainty quantification.

• Plug-and-Play: Fully backward compatible and fits seamlessly into existing workflows.