> I can bring a more accurate wind speed sensor to calibrate the windmill against the wind [...] But what should the prior be? [...] I have to have a crisp numb

> I can bring a more accurate wind speed sensor to calibrate the windmill against the wind [...] But what should the prior be? [...] I have to have a crisp number — a full distribution actually, accurate out to the tails. What would you do when the sensor returns negative wind speeds due to noise or errors? The wind speed cannot be negative, or greater than speed of light. An expert in windmill can narrow down the prior distribution much more. > But priors can be much harder. Choosing a prior is hard because it requires thinking explicitly about the problem and its assumptions. It merely exposes our lack of expertise on the problem. When you're lazy, you can pick a uniform prior Uniform(0, c) and call it a day. > We can sharpen the problem. Say my number and yours are different. How do we tell who is right? Forget about prior, say, we have 2 sensors which output two slightly different wind speeds. Which wind speeds is right? The lower one or the average speed. This is a deep philosophical problem. However, it's a problem for any model. > The prior will mostly wash out in any well-posed problem. I don't think so. Any well-posed problem should include the prior, or else how can we tell: 2 data points is not enough? > HN tends to invoke the Bayesian framework as a complete solution to inference [...] Bayesian framework is indeed a complete solution to inference in a formal/logical sense. However, I agree that there are many problems in applying Bayesian framework to real world problems that requires serious thinking about our assumptions on the problem.