Bayesian Approaches To The Cold Start Problem

John Cook explains what you can do with data-driven applications when you don’t yet have the data:

How do you operate a data-driven application before you have any data? This is known as the cold start problem.

We faced this problem all the time when I designed clinical trials at MD Anderson Cancer Center. We used Bayesian methods to design adaptive clinical trial designs, such as clinical trials for determining chemotherapy dose levels. Each patient’s treatment assignment would be informed by data from all patients treated previously.

But what about the first patient in a trial? You’ve got to treat a first patient, and treat them as well as you know how. They’re struggling with cancer, so it matters a great deal what treatment they are assigned. So you treat them according to expert opinion. What else could you do?

Read on for John’s solution.

Related Posts

Linear Programming in Python

Francisco Alvarez shows us an example of linear programming in Python: The first two constraints, x1 ≥ 0 and x2 ≥ 0 are called nonnegativity constraints. The other constraints are then called the main constraints. The function to be maximized (or minimized) is called the objective function. Here, the objective function is x1 + x2. Two classes of […]

Read More

Exploratory Data Analysis with inspectdf

Laura Ellis continues a dive into Exploratory Data Analysis, this time using the inspectdf package: I like this package because it’s got a lot of functionality and it’s incredibly straightforward to use. In short, it allows you to understand and visualize column types, sizes, values, value imbalance & distributions as well as correlations. Better yet, […]

Read More

Categories

August 2018
MTWTFSS
« Jul Sep »
 12345
6789101112
13141516171819
20212223242526
2728293031