Bayes' Rule - Artificial Intelligence for RoboticsYouTube↗  IA↗(long shot)
Education  12.7K views 8 likes 4 dislikes   02 Mar 2012
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I'm now going to quiz you on Bayes Rule.
ベイズの定理についての小テストです
Say you own a house, and you know that the house might catch fire in your absence,
例えばあなたには持ち家があり 留守中に家が火事になるかもしれません
but the probability of it catching fire--"f" over here--is small.
火事になる確率はfとします これは小さい値です
It's a 10th of a percent--0.001.
1パーセントの10分の1つまり0．001です
Let's say every afternoon you talk to your neighbor, and every afternoon you ask your neighbor, "Does my house burn?"
あなたは毎日午後に隣人に “私の家は燃えていますか？”と尋ねます
Of course,you a little bit paranoid if you do this, but for the sake of the argument, let's just assume you do this every afternoon.
変に思われるかもしれませんが 毎日その行動を繰り返すと仮定しましょう
This afternoon he comes back and says, "Yes, it burns," so B.
ある日隣人があなたの家を見て “燃えています”と返事します
You happen to know that the neighbor is not very truthful.
ですが実はその隣人はあまり信用できません
In fact, every time you ask him a question,
あなたが何か質問をする度に
you know there is a 0.1 chance--a 10% chance--the neighbor will just produce a lie
0．1つまり10％の確率で隣人はウソをつきます
and a 0.9 chance the neighbor actually speaks the truth.
そして0．9の確率で隣人は真実を答えます
So you ask him exactly one question--"Does my house burn?"
隣人に1つだけ次の質問をします “私の家は燃えていますか？”
He says, "Yes, it burns," but you know that the probability of this being a lie is 0.1.
彼は“燃えています”と答えますが これがウソである確率は0．1です
So in applying Bayes Rule I like to first compute the non-normalized posterior--p bar--
ベイズの定理を応用するには まず非正規化の事後分布を計算します
bar now stands for non-normalized--of fire given the neighbor just called.
このPバーは非正規化を指していて 隣人の言うとおり火事である確率を計算します
The same for the opposite event of no fire given the neighbor just said, yes, it burns.
次に隣人の答えに反し 本当は火事ではなかった確率を計算します
After you've done this, I'd like you to compute the normalized values
これができたら正規化の値を計算しましょう
that have to add up to 1.
合計すると1になります
Please enter all 4 values for this homework assignment.
この4つの値をすべて答えてください
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