Solving White-Box Problems#

Max Kochurov

Principal Data Scientist @ PyMC Labs

My core principles are:

  • Be Focused. I work exclusively with interpretable models and create transparent solutions.

  • Be Erudite. I have wide machine learning expertise to know when you need Bayesian approach.

  • Be Oriented. I focus on core needs first, that is where a good project starts.

  • Share knowledge. I love to educate about the statistical model, so you are familiar with it.

  • Be Helpful. If you do not need Bayesian inference I can still help you find a research direction.

Contact Me


When Bayesian#

Is your problem white box?

If your problem is not White Box it makes little sense to use Bayesian methods. The approach relies on clear understanding and reasoning about the problem.

How much do you know about the problem?

The more you understand about the model and process it describes the better is the solution. No traditional approach takes this to the absolute and translates expert opinions to rigor definition.

Do you understand causal relations of your problem?

Understanding relations is a half-way to build a Bayesian model. Ignoring these relations leads to biased estimates and decisions.

Do you need to interpret the solution?

Clear interpretation is the core principle in Bayesian modeling. Every unknown parameter in the model is estimated using expert and data guidance. Using Bayesian approach you make maximum use of human in the loop data driven business processes.

Do you need just predictions or something more?

If predictions is all you care about, I suggest looking into Conformal Predictions. It helps to take in account uncertainty for the prediction tasks. If this does not help, and you need uncertainty in model interpretation for your decision process, Bayesian approach is for you.

How I work#

Before any project starts:

  • We discuss what are the real needs for you

  • We figure out if project worth the time investment or there is a simple workaround

  • We investigate how to maximize the outcome of the collaboration, split the needs to options

Worst Case

Not every project is about beautiful models. If we do not fit each other, be sure I do my best to advice you the next steps to the best of my knowledge.

Book a Call


Here are my materials that may interest you:

  • I created modern Practical Bayes course for MSU. It focuses on practical interpretable analytics and model analysis.

  • The course is recorded on YouTube in State of Bayes playlist.

For more learning materials follow the link below:

Learn Bayes




I regularly participate in conferences, give talks and share knowledge about Bayesian statistics

I link all the available materials:

  • Video

  • Presentation

  • Code