Unit 11 03.mp4 Bayes Network of HMMs.mp4 knowitvideos English Japanese

Unit 11 03.mp4 Bayes Network of HMMs.mp4YouTube↗ IA↗(long shot)
Film & Animation 10.7K views 34 likes 0 dislikes 11 Nov 2011

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[Thrun] The essence of HMMs are really simply characterized

HMMの本質はこのようなベイジアンネットワークで

by the following Bayes network.

単純に表すことができます

There's a sequence of states that evolve over time,

時間とともに遷移していく状態の列があり

and each state depends only on the previous state in this Bayes network.

それぞれの状態はネットワークの 1つ前の状態のみに依存します

Each state also emits what's called a measurement.

状態はそれぞれ観測された記号を出力します

It is this Bayes network that is the core of hidden Markov models

このベイジアンネットワークは隠れマルコフモデルや

and various probabilistic filters such as Kalman filters, particle filters, and many others.

カルマンフィルタやパーティクルフィルタなど様々な確率的フィルタの核となっています

These are words that might sound cryptic and they might not mean anything to you,

こうした言葉は暗号のようで分かりにくいかもしれませんが

but you might come across them as you study different disciplines of computer science

コンピュータ科学や制御理論の手法について学ぶ際に

and control theory.

また遭遇することになるでしょう

The real key here is the graphical model.

このネットワークの鍵はこのグラフィックモデルです

If you look at the evolution of states,

この状態の遷移を見れば

what you'll find is that these states evolve as what's called a Markov chain.

これがマルコフ連鎖だと気づくでしょう

In a Markov chain, each state only depends on its predecessor.

マルコフ連鎖ではそれぞれの状態は 1つ前の状態のみに依存します

So for example, state S3 is conditioned on S2 but not on S1.

例えばS₃はS₂のみに依存しS₁には依存しません

It's only immediate through S2 that S3 might be influenced by S1.

S₃はS₂を介してのみS₁の影響を受けます

That's called a Markov chain, and we're going to study Markov chains quite a bit in this class to understand them well.

これがマルコフ連鎖と呼ばれるものでこの講座でもう少し詳しく学んでいきます

But what makes it a hidden Markov model or hidden Markov chain, if you wish,

隠れマルコフモデルや隠れマルコフ連鎖では

is the fact that there is measurement variables.

実際に観測変数が生成されます

So rather than being able to observe the state itself, what you get to see are measurements.

従って状態は直接外から観測できず変数である出力記号のみ観測可能です

Let me put this to perspective, showing you several of the robots I've built

実際に私が作った隠れた状態を持つロボットを例に

that possess hidden state.

詳しく見てみましょう

And where I only get to observe certain measurements,

いくつかの出力記号だけを観測できる状態で

let me infer something about the hidden state.

隠れた状態について推測してみましょう

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