Tag: Bayesian

Bayesian thinking is extremely powerful when it comes to software engineering. At its core Bayes Theorem is a way of updating our beliefs about the world with new knowledge that we gain. Bayes’ Theorem is extremely powerful because it allows us to use the additional specific information we ...

Bayesian thinking is extremely powerful when it comes to software engineering. At its core Bayes Theorem is a way of updating our beliefs about the world with new knowledge that we gain. Bayes’ Theorem is extremely powerful because it allows us to use the additional specific information we ...

I will do this with a small Bayesian Network (and some brute force). Each node will represent a choice (we need a zero since not everything is chosen) with the price of each item. Is there a way of choosing some subset of the appetizers to equal 15.05? Learn More

Optimizing a function is super important in many of the real life analytics use cases. By optimization we mean, either find an maximum or minimum of the target function with a certain set of parameter combination. Finding out that min or max value as well as the parameters should be the objective. I...

If someone says, “I believe in science”, what do you think of this statement? Initially, I found it funny because science is not something we believe, but something we pursue for the understanding of the natural world based on evidence, mainly through observation, right? But I am startin...

Introduction to Bayesian Inference Bayesian inference serves as the theoretical backbone of Bayesian Model Fitting, grounded in the concept of updating our beliefs in the light of new evidence. Central to this process is the prior distribution, p(θ∣H), which encapsulates our initial ...

 consider a function f that is inaccessible to us. We cannot directly access f or compute its gradients. Our only available information is providing an input x and receiving a noisy estimation (or without any noise) of the true output. Our objective is to op...

If you’ve ever felt a bit intimidated by the world of statistics, especially Bayesian statistics, you’re not alone. But fear not! In this article, we’ll take a down-to-earth approach to introduce you to Bayesian statistics using the powerful PyMC3 library. No fancy jargon, just a s...

Randomized experiments, a.k.a. AB tests, are the established standard in the industry to estimate causal effects. Randomly assigning the treatment (new product, feature, UI, …) to a subset of the population (users, patients, customers, …) we ensure that, on average, the difference...

The Bayesian vs Frequentist debate is one of those academic arguments that I find more interesting to watch than engage in. Rather than enthusiastically jump in on one side, I think it’s more productive to learn both methods of statistical inference and apply them where app...

The beauty of Bayesian statistics is, at the same time, one of its most annoying features: we often get answers in the form of “well, the number is somewhere between…” If on one hand, this might be frustrating to business strategists or policymakers looking for straightforward ...