[ja] cs-229-probability #142

nao0811ta · 2019-05-25T06:06:10Z

I've finished translating. please, someone, review this.

shervinea · 2019-05-29T07:36:21Z

Great work @nao0811ta, thank you for all your time! Let's start the review process now!

nao0811ta · 2019-05-29T23:29:57Z

@shervinea Gogo. Could you please set review label? I don’t have permission to set it.

shervinea · 2019-05-30T01:32:03Z

@nao0811ta Done! Please feel free to invite anyone you know who may review your work.

suzana-ilic · 2019-06-03T04:01:17Z

Hi @shervinea all of the cheatsheets will be translated into Japanese by MLT members - if you set the label to "reviewer wanted" for the rest as well I can send out an open call! Thanks so much!

shervinea · 2019-06-03T04:11:28Z

@suzana-ilic that's awesome, thank you so much for your help! As of now, we are looking for reviewers for:

[ja] cs-230-recurrent-neural-networks #146: Recurrent neural networks
[ja] cs-230-deep-learning-tips-and-tricks #157: Deep learning tips and tricks
[ja] cs-229-deep-learning #96: Deep learning
[ja] cs-229-probability #142: Probabilities and Statistics

current work in progress are:

[ja] cs-229-supervised-learning #144: Supervised learning
[ja] cs-229-machine-learning-tips-and-tricks #99: Machine learning tips and tricks
[ja] cs-229-linear-algebra #140: Linear Algebra
[ja] cs-230-convolutional-neural-networks #145: Convolutional neural networks

and the translation yet to be started is:

Unsupervised learning

ytknzw · 2019-08-16T10:53:47Z

Hi @shervinea and @suzana-ilic
I review #142: Probabilities and Statistics.

ytknzw

Review for part 1 - 38.

ytknzw · 2019-08-16T10:57:14Z

ja/refresher-probability.md

@@ -0,0 +1,381 @@
+**1. Probabilities and Statistics refresher**
+
+&#10230;確率と統計


This should be "確率と統計の復習" to include "refresher" into translation.

ytknzw · 2019-08-16T10:59:51Z

ja/refresher-probability.md

+
+**2. Introduction to Probability and Combinatorics**
+
+&#10230;確率と組合せの紹介


In this case, "introduction" could be better translated as "導入" or "初歩" than "紹介".

ytknzw · 2019-08-16T11:25:45Z

ja/refresher-probability.md

+
+**3. Sample space ― The set of all possible outcomes of an experiment is known as the sample space of the experiment and is denoted by S.**
+
+&#10230;標本空間 - 試行可能なすべての結果の集合は標本空間として知られ、Sと表します。


"試行可能なすべての結果" -> "ある試行のすべての起こりうる結果"
"標本空間として知られ" -> "その試行の標本空間として知られ"

ytknzw · 2019-08-16T11:27:24Z

ja/refresher-probability.md

+
+**4. Event ― Any subset E of the sample space is known as an event. That is, an event is a set consisting of possible outcomes of the experiment. If the outcome of the experiment is contained in E, then we say that E has occurred.**
+
+&#10230;事象 - 標本空間のすべての部分集合のEを事象と言います。つまり事象は試行可能な結果で構成された集合です。試行結果がEに含まれるなら、Eが発生した言います。


"すべての部分集合のE" -> "任意の部分集合E": this change could make the intention of this sentence clearer.
"つまり事象は" -> "つまり、ある事象は"
"試行可能な結果で" -> "ある試行の起こりうる結果により"
"試行結果が" -> "ある試行結果が"
"発生した言います" -> "起きたと言います"

ytknzw · 2019-08-16T11:33:13Z

ja/refresher-probability.md

+
+**6. Axiom 1 ― Every probability is between 0 and 1 included, i.e:**
+
+&#10230;公理1 - すべての確立は0と1の間に含まれ次のようになります：


"確立" -> "確率"
"0と1の間に含まれ" -> "0と1を含んでその間にあります。"
"次のようになります：" -> "すなわち："

ytknzw · 2019-08-19T11:47:54Z

ja/refresher-probability.md

+
+**34. Main distributions ― Here are the main distributions to have in mind:**
+
+&#10230;主な分布 - 覚えておくべき主な分布があります:


"覚えておくべき主な分布があります" -> "覚えておくべき主な分布をここに挙げます"

ytknzw · 2019-08-19T11:50:32Z

ja/refresher-probability.md

+
+**35. [Type, Distribution]**
+
+&#10230;[タイプ、分布]


"タイプ" -> "種類" To keep consistency with part 26.

ytknzw · 2019-08-19T11:52:13Z

ja/refresher-probability.md

+
+**36. Jointly Distributed Random Variables**
+
+&#10230;結合確率変数


"結合確率変数" -> "同時分布の確率変数"
The term "同時分布" would be more popular than "結合分布".

ytknzw · 2019-08-19T11:57:37Z

ja/refresher-probability.md

+
+**37. Marginal density and cumulative distribution ― From the joint density probability function fXY , we have**
+
+&#10230;周辺密度と累積分布 - 結合密度確率関数fXYから次のようになります。


"結合" -> "同時"
"密度確率関数" -> "確率密度関数" ('Density probability function' should be 'probability density function'.)
"次のようになります" -> "次が成り立ちます"

ytknzw · 2019-08-19T11:58:12Z

ja/refresher-probability.md

+
+**38. [Case, Marginal density, Cumulative function]**
+
+&#10230;[例,、周辺密度、累積関数]


"例" -> "種類"

ytknzw

Review for the rest (from part 39 to the end).

ytknzw · 2019-08-28T00:15:13Z

ja/refresher-probability.md

+
+**39. Conditional density ― The conditional density of X with respect to Y, often noted fX|Y, is defined as follows:**
+
+&#10230;条件部密度(Conditional density) - Yに対するXの条件部密度は主にfx|Yと表記され、次のように定義されます:


"条件部密度" -> "条件付き密度"
"主に" -> "よく"
"fx|Y" -> "fX|Y"

ytknzw · 2019-08-28T00:17:02Z

ja/refresher-probability.md

+
+**40. Independence ― Two random variables X and Y are said to be independent if we have:**
+
+&#10230;独立性(Independence) - 二つの確率変数XとYは次の場合、独立的と言います。


"二つの" -> "2つの" for consistency.
"次の場合" -> "次が成り立つとき"
"独立的" -> "独立である"
"言います。" -> "言います:"

ytknzw · 2019-08-28T00:23:21Z

ja/refresher-probability.md

+
+**41. Covariance ― We define the covariance of two random variables X and Y, that we note σ2XY or more commonly Cov(X,Y), as follows:**
+
+&#10230;共分散(Covariance) - 次のようにふたつの確率変数X,Yの共分散をσ2XYまたはさらに一般的にはCov(X,Y)で定義します。


"ふたつの" -> "2つの" for consistency.
"X,Yの" -> "XとYの"
"次のようにふたつの確率変数X,Yの共分散をσ2XYまたはさらに一般的にはCov(X,Y)で定義します。" -> "2つの確率変数XとYの共分散を、σ2XYまたはより一般的にはCov(X,Y)と表記し、次のように定義します:"

ytknzw · 2019-08-29T10:21:28Z

ja/refresher-probability.md

+
+**42. Correlation ― By noting σX,σY the standard deviations of X and Y, we define the correlation between the random variables X and Y, noted ρXY, as follows:**
+
+&#10230;相関関係(Correlation) - X, Yの標準変数をσX,σYで表記し、確率変数X,Yの相関関係をρXYで表記し、次のように定義します。


"相関関係" -> "相関係数"
"標準変数" -> "標準偏差"
"で表記し" -> "と表記し"
"定義します。" -> "定義します:"

ytknzw · 2019-08-29T10:25:17Z

ja/refresher-probability.md

+
+**43. Remark 1: we note that for any random variables X,Y, we have ρXY∈[−1,1].**
+
+&#10230;注釈 1: 任意の確率変数X,Yに対してρXY∈[−1,1]となります。


"となります" -> "が成り立ちます"

ytknzw · 2019-08-29T11:12:28Z

ja/refresher-probability.md

+
+**57. Remark: the sample variance is unbiased, i.e E[s2]=σ2.**
+
+&#10230;注釈: 標本分散は不偏です。つまりE[s2]=σ2になります。


"つまり" -> "すなわち" for consistency.
"になります。" -> "が成り立ちます。"

ytknzw · 2019-08-29T11:21:02Z

ja/refresher-probability.md

+
+**58. Chi-Squared relation with sample variance ― Let s2 be the sample variance of a random sample. We have:**
+
+&#10230;標本分散とカイ二乗の関係 - 確率標本の標本分散をs2としよう。次のようになります。


"カイ二乗の関係" -> "カイ二乗分布との関係"
"としよう。次のようになります。" -> "とすると、次が成り立ちます:"

ytknzw · 2019-08-29T11:22:07Z

ja/refresher-probability.md

+
+**59. [Introduction, Sample space, Event, Permutation]**
+
+&#10230;[紹介、標本空間、事象、順列]


"紹介" -> "導入"

ytknzw · 2019-08-29T11:23:07Z

ja/refresher-probability.md

+
+**60. [Conditional probability, Bayes' rule, Independence]**
+
+&#10230;[条件部確率、ベイズの定理、独立]


"条件部確率" -> "条件付き確率"

ytknzw · 2019-08-29T11:24:50Z

ja/refresher-probability.md

+
+**63. [Jointly distributed random variables, Density, Covariance, Correlation]**
+
+&#10230;[結合分布の確率変数、密度、共分散、相関関係]


"結合分布の確率変数" -> "同時分布の確率変数" for consistency.
"相関関係" -> "相関係数"

ytknzw · 2019-08-29T11:28:37Z

Hi @shervinea and @nao0811ta
Review for #142: Probabilities and Statistics is finished.

shervinea · 2019-09-03T04:51:51Z

Thank you @ytknzw and @nao0811ta for your thorough work!
@nao0811ta please feel free to let me know when you think the PR is ready to be merged.

suzana-ilic · 2019-09-09T11:20:35Z

Hi @nao0811ta! Just a quick reminder, please take a look a the comments and let us know when it's ready to be merged! Thanks so much! :)

nao0811ta · 2019-09-10T14:01:35Z

@ytknzw san
thank you for your work sincerely.

nao0811ta · 2019-09-10T14:29:00Z

@ytknzw, @suzana-ilic
I've just finished to fix all review comment and checked 2 times.
PTAL and feel free to merge if it's ok.
thank you.

shervinea · 2019-09-11T05:36:31Z

Thank you both @nao0811ta and @ytknzw for your amazing work. Merging the PR right now.

Update refresher-probability.md

7c8e6ff

nao0811ta force-pushed the feature-ja-probability branch from b68a990 to 7c8e6ff Compare May 26, 2019 14:09

shervinea added the reviewer wanted Looking for a reviewer label May 30, 2019

ytknzw reviewed Aug 19, 2019

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ytknzw reviewed Aug 29, 2019

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nao0811ta force-pushed the feature-ja-probability branch 2 times, most recently from 69913cf to 3697a1d Compare September 10, 2019 14:13

fixed review comment

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nao0811ta force-pushed the feature-ja-probability branch from 3697a1d to f2bc0bd Compare September 10, 2019 14:27

shervinea merged commit 09afafd into shervinea:master Sep 11, 2019

shervinea changed the title ~~[ja] Probabilities and Statistics~~ [ja] cs-229-probability Oct 6, 2020

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[ja] cs-229-probability #142

[ja] cs-229-probability #142

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shervinea commented Sep 11, 2019

		@@ -0,0 +1,381 @@
		1. Probabilities and Statistics refresher

		⟶確率と統計


		2. Introduction to Probability and Combinatorics

		⟶確率と組合せの紹介


		3. Sample space ― The set of all possible outcomes of an experiment is known as the sample space of the experiment and is denoted by S.

		⟶標本空間 - 試行可能なすべての結果の集合は標本空間として知られ、Sと表します。


		4. Event ― Any subset E of the sample space is known as an event. That is, an event is a set consisting of possible outcomes of the experiment. If the outcome of the experiment is contained in E, then we say that E has occurred.

		⟶事象 - 標本空間のすべての部分集合のEを事象と言います。つまり事象は試行可能な結果で構成された集合です。試行結果がEに含まれるなら、Eが発生した言います。


		6. Axiom 1 ― Every probability is between 0 and 1 included, i.e:

		⟶公理1 - すべての確立は0と1の間に含まれ次のようになります：


		34. Main distributions ― Here are the main distributions to have in mind:

		⟶主な分布 - 覚えておくべき主な分布があります:


		36. Jointly Distributed Random Variables

		⟶結合確率変数


		37. Marginal density and cumulative distribution ― From the joint density probability function fXY , we have

		⟶周辺密度と累積分布 - 結合密度確率関数fXYから次のようになります。


		38. [Case, Marginal density, Cumulative function]

		⟶[例,、周辺密度、累積関数]


		39. Conditional density ― The conditional density of X with respect to Y, often noted fX\|Y, is defined as follows:

		⟶条件部密度(Conditional density) - Yに対するXの条件部密度は主にfx\|Yと表記され、次のように定義されます:


		35. [Type, Distribution]

		⟶[タイプ、分布]


		40. Independence ― Two random variables X and Y are said to be independent if we have:

		⟶独立性(Independence) - 二つの確率変数XとYは次の場合、独立的と言います。


		41. Covariance ― We define the covariance of two random variables X and Y, that we note σ2XY or more commonly Cov(X,Y), as follows:

		⟶共分散(Covariance) - 次のようにふたつの確率変数X,Yの共分散をσ2XYまたはさらに一般的にはCov(X,Y)で定義します。


		42. Correlation ― By noting σX,σY the standard deviations of X and Y, we define the correlation between the random variables X and Y, noted ρXY, as follows:

		⟶相関関係(Correlation) - X, Yの標準変数をσX,σYで表記し、確率変数X,Yの相関関係をρXYで表記し、次のように定義します。


		43. Remark 1: we note that for any random variables X,Y, we have ρXY∈[−1,1].

		⟶注釈 1: 任意の確率変数X,Yに対してρXY∈[−1,1]となります。


		57. Remark: the sample variance is unbiased, i.e E[s2]=σ2.

		⟶注釈: 標本分散は不偏です。つまりE[s2]=σ2になります。


		58. Chi-Squared relation with sample variance ― Let s2 be the sample variance of a random sample. We have:

		⟶標本分散とカイ二乗の関係 - 確率標本の標本分散をs2としよう。次のようになります。


		59. [Introduction, Sample space, Event, Permutation]

		⟶[紹介、標本空間、事象、順列]


		60. [Conditional probability, Bayes' rule, Independence]

		⟶[条件部確率、ベイズの定理、独立]


		63. [Jointly distributed random variables, Density, Covariance, Correlation]

		⟶[結合分布の確率変数、密度、共分散、相関関係]

[ja] cs-229-probability #142

[ja] cs-229-probability #142

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