vinayak
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I have read this post before and have agreed to it. But I read it again just now and have new doubts.
I still agree that beliefs should pay rent in anticipated experiences. But I am not sure any more that the examples stated here demonstrate it.
Consider the example of the tree falling in a forest. Both sides of the argument do have anticipated experiences connected to their beliefs. For the first person, the test of whether a tree makes a sound or not is to place an air vibration detector in the vicinity of the tree and check it later. If it did detect some vibration, the answer is yes. For the... (read more)
How about this:
People are divided into pairs. Say A and B are in one pair. A gets a map of something that's fairly complex but not too complex. For example, an apartment with a sufficiently large number of rooms. A's task is to describe this to B. Once A and B are both satisfied with the description, B is asked questions about the place the map represented. Here are examples of questions that could be asked:
How many left-turns do you need to make to go from the master bed room to the kitchen?
Which one is the washroom nearest to the game room?
You are sitting in room1 and you want to go to... (read more)
I will come too.
Hey, I live in Waterloo too. I will join. (Perhaps not this one, but any subsequent ones after the 24th this month that are organized in Waterloo.) Please keep me posted and let me know if you need any help in organizing this.
Pretty neat. Thanks!
If you have many things to do and you are wasting time, then you should number those things from 1 to n and assign n+1 to wasting time and then use http://random.org to generate a random number between 1 and n+1 (1 and n+1 included) to decide what you should do. This adds some excitement and often works.
I live in Waterloo, Ontario (Canada). Does anyone live nearby?
I'm in too.
Consulting a dataset and counting the number of times the event occured and so on would be a rather frequentist way of doing things. If you are a Bayesian, you are supposed to have a probability estimate for any arbitrary hypothesis that's presented to you. You cannot say that oh, I do not have the dataset with me right now, can I get back to you later?
What I was expecting as a reply to my question was something along the following lines. One would first come up with a prior for the hypothesis that the world will be nuked before 2020. Then, one would identify some facts that could be used as evidence in favour or against the hypothesis. And then one would do the necessary Bayesian updates.
I know how to do this for the simple cases of balls in a bin etc. But I get confused when it comes to forming beliefs about statements that are about the real world.
Haha! Very curious to know how this turns out!