Bayesian inference and frequentist methods are two different ways to analyze data. Each has its own challenges.

1. Subjectivity vs. Objectivity:

• Bayesian methods need something called prior distributions. This means they can include personal opinions, which might affect the results.
• Frequentist methods rely on objective data. This means they try to be neutral but don’t usually use any past information.

2. Computational Complexity:

• Bayesian models can be complicated and take a lot of computing power, especially when dealing with big data or complex situations.
• Frequentist methods often use simpler techniques, but they might miss some of the uncertainties in the data.

3. Interpretability:

• Bayesian results are shown as probabilities. This can be easier for some people to understand, but it can be tough to explain to those who aren’t experts.
• Frequentist confidence intervals can be harder for many people to understand correctly.

To help with these issues, there are now easier-to-use Bayesian tools, like PyMC and Stan. There are also more educational resources available. This makes Bayesian inference easier to use and understand.