Ask HN: Bayesian FTW
Reading this HN entry (https://news.ycombinator.com/item?id=9780677) has rekindled my interest in Bayesian logic.
My main introduction to the topic was through original Stanford AI MOOC.
I managed to get decent scores on all the Bayesian-related stuff, but I cannot say I really internalize the ideas.
What I am looking for is something that could help me making practical use of Bayes in my day-to-day life (professional and/or personal).
So I am basically thinking of:
- books or articles detailing practical examples of how to apply Bayesian models to day-to-day choices (if these covered debug and testing activities it would be a big plus) - an easy-to-use app (desktop or tablet/smartphone) to build and play with Bayesian networks models.
Of course, if you have anything else to suggest along the same lines, please do.
9 comments
[ 4.1 ms ] story [ 53.3 ms ] threadYour set might look something like this:
Category - Document
negative - I don't like ice cream
positive - That's an awesome idea
neutral - The wind is blowing today
positive - We won!
You'll get a categorization of either positive, negative or neutral.
I do manage my bank account, but I don't see any need for an "classifier": when I input (or check) a supermarket bill I don't need much help in setting it to "GROCERIES". Basically anything that gets into my accounting files it's either something I know already what is about, or else something I need to investigate (e.g.: a speed ticket from a foreign country, routed to my credit card by the car rental company).
I see Bayes as a method to solve certain classes of problem but the key challenge in everyday decisions is more like a design problem i.e. to define what the problem is. Once you have defined "what would be a good outcome of this decision" sufficiently, the answer rarely requires statistical methods. The common sources of error in a decision are in its definition e.g. omitting a requirement that is later revealed as essential.
For example, if you are analysing drug studies, Bayes is obviously relevant but for something more common place such as choosing a software tool, the main challenge is to understand what your goals are so that you can identify the criteria by which to judge the tools and the trade-offs you are willing to accept.
The biggest steps to improving decision quality appear to be process related e.g. using prototypes to explore options before making a larger commitment. Such acts are so effective because they reveal information that lets you improve your goals rather than just clarifying the quality of an option.
I am curious if there is any kind of resource that would help me to confidently add Bayes to my normal decision-making toolchest.
I'd love to know how your ideal tool might work. Do you mind if I drop you an email (from your profile info)?
I'm working on a decision making tool to let you quickly describe reasoning with simple bullet point lists. It aims to be as quick as a making a rough note so that with just a few different types of bullet, you can create belief networks and computable decision models.
The tool is intended for a general audience so it currently uses simple weighted evidence. If you can tell me how you would like to work with probabilities then you might get the tool you want :)
It details how to use the PyMC library via iPython notebooks. I'm not sure if iPython qualifies as an easy to use app though.