I honestly don't believe Bayes is that relevant to day to day decision making but I'm keen to see a counter-argument (I have some but I don't find them convincing yet).

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.

Well, I wouldn't be surprised if the only practical benefit in building a Bayesian Network model ended up being that "that you had to think long and hard at what the various inputs are and how they should be linked".

I am curious if there is any kind of resource that would help me to confidently add Bayes to my normal decision-making toolchest.

Yep, I can see the value as a thinking tool. There are tools like http://www.bayesia.com/ and http://www.hugin.com/ but they are enterprise pricey and I find them very clunky.

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 :)

Sorry, seen this only now... please feel free to write directly, thanks.

I find that having a bayes classifier handy can help a lot with categorizing documents or text. I manually keep track of my bank accounts using a spreadsheet, and I started using a classifier to automatically categorize the transaction. It's quite a time saver.

Can you explain what you use as categorization elements?

You train your own set by posting the document and the classification / category to the service. The training set is then used to calculate the prior probabilities needed by Bayes theorem. Once you have a proper training set, you can post a document to the service and it will return a list of probable categories from the training set.

Your 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.

Yes, well - I understand the general theory, but I don't understand how it applies to managing your bank account.

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've had this one stacked up on my reading list for a while: http://camdavidsonpilon.github.io/Probabilistic-Programming-...

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.