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数学 高校生

2007年東大 確率 (3)のm=nのときの確率が1にならないのは何故ですか? 2度とも高さはmになるので、高い方のブロックの高さがmである確率は1になる気がします… 教えて下さい🙇

[19] No 1 確率の応用③ VV ① ブロックの高さは, 最初は 0 とする。 9/100592105 表が出る確率が♪, 裏が出る確率が1-0であるような硬貨がある。ただし, 01 する。この硬貨を投げて,次のルール(R)の下で,ブロック積みゲームを行う。 (2) (ア)manのとき、 No. (1)m=nのとき、 (R) ② 硬貨を投げて表が出れば高さ1のブロックを1つ積み上げ, 最後の高さがm以下(n) となるのは、 裏が出ればブロックをすべて取り除いて高さ0に戻す。 (1)で,最後にブロックの高さがm以下となる確率を求めよ。 nを正の整数, m を0≤m≦n を満たす整数とする。 V (1) n回硬貨を投げたとき、最後にブロックの高さが となる確率 m を求めよ。 (3) ルール(R)の下で, n回硬貨投げを独立に2度行い,それぞれ最後のブロックの高さ を考える。2度のうち, 高い方のブロックの高さがmである確率 1m を求めよ。 ただ し,最後のブロックの高さが等しいときはその値を考えるものとする。 F m Sapk 211-90190k = (1-9)x+1 bm=100m+1 1-9 よって、 9m + gm=am=1 11-gmt (0 ≤m≤n-1) (m=n) (東京大) 2007 n-m (1-9 n -X0000 m ☆互いに排反or場合分けで注意 (3)条件をみたすのは、 19 (1)裏が出ると、高さがCの状態、つまり最初の 状態に戻るので、裏が少なくとも1回出るか どうかで場合分け よって、口回投げたとき最後の高さがいか、 □未満かで場合分け 1回2回 n-m@ (ア) △ (イ) ○○ X 00 ma no △:注意 0:表… X:1-9 www (ア) m≠nのとき. Pm=(1-ppp (1)m=nのとき、 Pm=Pn=" (1-90) 9pm (0 ≤m≤n-1) よって、Pm (m=n) 「2度とも以下」から「2度ともM-1以下 mis を取り除いた場合 (ア)manつまり0≧m≦n-1のとき 2 m=9m² 70m² (m40) hm-1 = (1-70+172-(1-90112 F = 12-7pm 9pm 1pm 1-P+1) い また、m=0のとき、911-90ドリ m=0のときも成り立の (1)m=nのとき、 2 = 2-02 ym よって、 2 1回 2回 m m m m-1以下 m-14 m とも m以 -m-132F 高い方が M (2-9pm-p")" (-1+1) (0εmsn-1) Ym9pm (2-90m) 1-11-9 Q2回のうちのMexより、ドーナツ型 =9pm (2-9pm) 2 941ブロックの高さが1以下となる確率 (man) #

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TOEIC・英語 大学生・専門学校生・社会人

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15 語数: 398 語 出題校 法政大 5 We are already aware that our every move online is tracked and analyzed. But you 2-53 couldn't have known how much Facebook can learn about you from the smallest of social interactions - a 'like'*. (1) Researchers from the University of Cambridge designed (2) a simple machine-learning 2-54 system to predict Facebook users' personal information based solely on which pages they had liked. E "We were completely surprised by the accuracy of the predictions," says Michael 2-55 Kosinski, lead researcher of the project. Kosinski and colleagues built the system by scanning likes for a sample of 58,000 volunteers, and matching them up with other 10 profile details such as age, gender, and relationship status. They also matched up those likes with the results of personality and intelligence tests the volunteers had taken. The team then used their model to make predictions about other volunteers, based solely on their likes. The system can distinguish between the profiles of black and white Facebook users, 15 getting it right 95 percent of the time. It was also 90 percent accurate in separating males and females, Democrats and Republicans. Personality traits like openness and intelligence were also estimated based on likes, and were as accurate in some areas as a standard personality test designed for the task. Mixing what a user likes with many kinds of other data from their real-life activities could improve these predictions even more. 20 Voting records, utility bills and marriage records are already being added to Facebook's database, where they are easier to analyze. Facebook recently partnered with offline data companies, which all collect this kind of information. This move will allow even deeper insights into the behavior of the web users. 25 30 (3) - Sarah Downey, a lawyer and analyst with a privacy technology company, foresees insurers using the information gained by Facebook to help them identify risky customers, and perhaps charge them with higher fees. But there are potential benefits for users, too. Kosinski suggests that Facebook could end up as an online locker for your personal information, releasing your profiles at your command to help you with career planning. Downey says the research is the first solid example of the kinds of insights that can be made through Facebook. "This study is a great example of how the little things you do online show so much about you,” she says. "You might not remember liking things, " but Facebook remembers and (4) it all adds up.", * a 'like': フェイスブック上で個人の好みを表示する機能。 日本語版のフェイスブックでは「いいね!」 と表記される。 2-56 2-57 2-58 36

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