"事象"の翻訳 英語に:


  辞書 日本-英語

キーワード : Events Horizon Occurred Event Incident

  例 (レビューされていない外部ソース)

最尤推定法ではデータ中の全事象と特定の事象
One technique to deal with the overfitting problem is called Laplace smoothing.
決めます この二つの事象は独立事象でしょうか
The next ticket is pulled out to determine the winner of the second prize.
負ける事象が2つで 全事象は3つだから 2 3 になる
Likewise, your probability of losing well, there are two ways you can lose out of three possibilities, going to be two thirds.
我々の複合事象の制約に適った根元事象は どれだけあるでしょうか 複合事象は 二つ以上の条件に適っている事象のことです
Now how many of these possibilities, how many of these outcomes, how many of these simple events meet the constraints of our compound event?
根元事象は 一つのみの結果に関連づけられている事象です
Compound event Fancy word for saying that there's more than one outcome that we can say that this event has happened.
全ての均等な事象の中で 条件Aに適っている事象の数です
So far we've been dealing with one way of Probability, that was the probability of (A) occur,
全ての均等な事象の中で 条件Aに適っている事象の数です
The number of events that satisfy A over all number of the equally likely events
全ての可能な事象のうち 条件に適う事象の数を数えるなら
But there aren't really two equally likely events
第一印象が大事である
First impressions are important.
つまり事象によっては
Written this way, it looks really clumsy, but that's effectively what we did when we went to the truth table.
1つは余事象確率です
Thrun So we just learned a number of things.
コイン投げはランダムな事象です
And once you've done this, I want you to compute the mean of the outcome and the standard deviation.
シャノン 2つの事象が起きて
JH Two stars, or
分析可能な全ての事象
Piano
これらは独立事象です
That's our third roll.
独立した事象とは何を意味するかを考えましょう それは 一つの事象の結果が二回目の事象の結果に
Now before we even think about this exact case, let's think about what it means for events to be independent.
表か裏を得る事象の総数は何でしょうか 表か裏を得る事象の総数は何でしょうか 事象の総数は2です
We could use that definition and say, well, what are the total number of events where I could get heads or tails, right?
二回目の事象 その結果は
So these are not independent events.
軍事用 気象予報用 通信用
These are a number of our science space Earth orbiters.
XとYは独立の事象です
We have 2 random variables, X and Y, whose probability individually is 0.2.
この時 この問題の 事象
For a single dice, these are the 6 possible faces.
これらのノードは既に明らかな事象 または未知の事象に対応しており
A Bayes network is composed of nodes.
事象が発生する場合の数で
So this is what the definition of the probability is.
次にある事象が起こりうる
6 x 6.
2つの事象をグラフ化しました
Here's the result of running the Greedy agent over 500 trials.
在シドニーの艦で起きた事象です
The incident occurred yesterday on the Sydney mother ship.
二つの同じ目を振るのは 複合事象です では描いていきましょう 複合事象
That's why we call it a compound event.
2つ目の事象もやってみます
They're together is 0.125.
この事象を数学的に導き出し
It has no preference whatsoever. That is really the result of moving many, many times.
2つの可能な事象はあります
Now we can't say there are two equally likely events
死に至る事象であり 状況です
Now this is of course not a disease per se.
いくつかな 3つの事象のうち
Well, what's the probability that you initially picked wrong?
サイコロゲームのような ランダムに起こる事象
It was not based on magic, but mathematics.
これらを独立事象にする方法は
So they are not independent.
一回目の事象の結果に依存しています つまり これらは独立事象ではありません
The second event the outcomes for it, are dependent on what happened in the first event.
二つとも同じ目の確率 これは複合事象です これは 二つ以上の事象が関連づけられています 根元事象が二つ以上関連づけられています
So if you want to figure out the probability of rolling doubles... ....the probability of rolling doubles, which is a compound event.... ...It has more than one event associated with it, more than one simple event associated with it.
今は相互排他事象について語る いい機会だと思います 一回目で弾く事象の確率に
And I think that's a good time now that we've drawn this tree and we've talked about two flips in a row, to realize that these are mutually exclusive events.
念力は物理的事象や過程における
(Laughter)
驚くべき現象ですが 事実なのです
There was no progress, no innovation.
実際には多くの事象 多くの結果が
So if we look at this way there's this probability.
根本的な事象に違いはありません
The key thing to realize is that the left handed coordinate system is equally valid.
これらの事象は パターン と呼ばれている
They're calling these events the pattern.
我々がまだ知らない3つの事象
Three of them are incidents that we haven't been aware of.
象徴色 象徴物
Symbolic Codes has three sons
確率0 1の事象に対する余事象の確率の計算を この関数にカプセル化することができました
So congratulations, you've implemented the very first example of probability where the event probability is 0.1 and the complementary event and negation of it is encapsulated in this function over here.