Bayes Image
Bayesian Approach
Using this kind of approach allows us to incorporate business know-how naturally in the form of priors, explore the space of models, and include uncertainty to find the best distribution of models, resulting in a more robust solution.
State-of-Art Bayesian Heuristic Methods
Using MCMC, SGD and Variational training methods enables automated training with initialization, sampling and stopping criteria.
Transport Gaussian Process Graph
Transport Gaussian Process
This kind of non-parametric process allows accurate modeling of the base sales, revealing structure in seemingly random behavior.
Our Workflow
Proprietary Algorithm Implementations
Our in-house built software library gives us full control and understanding over the algorithms and methods we implement, using open source libraries for low-level operations only.
Models are trained in stages, allowing us to keep focus on individual aspects one at a time. This also minimizes impact and reprocessing costs due to changes in scope or data.
Scale-Free Parameterization
All used data is transformed to a standard scale, creating a common modeling parameter space across all projects.
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