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\( E=\{a, b, c\} \) ا- اذا كانت X تمب سلسلa ماركوف بيبال حالة , \[ \begin{array}{l} P=\left[\begin{array}{ccc} 0 & 1 / 4 & 3 / 4 \\ 1 / 4 & 3 / 4 & 0 \\ 1 / 5 & 0 & 4 / 5 \end{array}\right] \\ P_{i}^{0}=(1 / 5,2 / 5,2 / 5) \quad \text {,التنـيع الاولي } \\ \text { الصسب تيمة ما يلي : } \\ \left(\mathrm{P}_{\mathrm{i}}^{3}\right),\left(\mathrm{P}_{\mathrm{i}}^{2}\right),\left(\mathrm{P}_{\mathrm{i}}\right)(\mathrm{i}) \\ P\left\{X_{1}=a, X_{2}=C, X_{3}=C, X_{4}=C, X_{5}=a / X_{0}=\right.\text { (ب) } \\ \text { C \} } \end{array} \]

Ask by Wells Kelley. in Iraq
Mar 11,2025

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الاحتمال المطلوب \( P\{X_{1} = a, X_{2} = c, X_{3} = c, X_{4} = c, X_{5} = a | X_{0} = b\} \) هو صفر.

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تاريخ نظم ماركوف يعود إلى أوائل القرن العشرين، عندما قام العالم الروسي أندريه ماركوف بدراسة سلاسل الأحداث العشوائية. صاغ مفهوم "سلسلة ماركوف" الذي يفيد بأن الحالة التالية تعتمد فقط على الحالة الحالية، وليس على الحوادث السابقة. هذه النظرية تم استخدامها في مجالات متعددة، من الإحصاءات إلى علم الحاسوب، مما جعلها ذات أهمية في التطبيقات الحديثة. تطبيقات ماركوف تمس حياتنا اليومية بطرق غير متوقعة. على سبيل المثال، تُستخدم نماذج ماركوف في تحليل البيانات، بما في ذلك توقعات الطقس وتصفية البريد المزعج. بالإضافة إلى ذلك، يمكن استخدامها في تطوير الألعاب، حيث تسهم في تحديد احتمالات الانتقال بين الحالات المختلفة. هذا يجعل فهم سلاسل ماركوف أمرًا مثيرًا ومفيدًا في عدة مجالات!

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If events \( A \) and \( B \) are from the same sample space, and if \( P(A)=0.8 \) and \( P(B)=0.9 \), can events \( A \) and \( B \) be mutually exclusive? Explain. Choose the correct answer below. A. All probabilities are less than or equal to 1 and greater than or equal to 0 . If events are mutually exclusive, then \( P(A \cup B)=P(A)+P(B) \). Therefore, if \( A \) and \( B \) are mutually exclusive, \( P(A \cup \) \( \begin{array}{l}B)=P(A)+P(B)=0.8+0.9=1 \text {, which is possible. Therefore, events } A \text { and } B \text { are mutually exclusive. } \\ \text { B. All probabilities are less than or equal to } 1 \text { and greater than or equal to } 0 \text {. If events are mutually exclusive, } \\ \text { then } P(A \cup B)=P(A)+P(B) \text {. Therefore, if } A \text { and } B \text { are mutually exclusive, } P(A \cup\end{array} \) \( B)=P(A)+P(B)=0.8+0.9=0 \), which is possible. Therefore, events \( A \) and \( B \) are mutually exclusive. C. All probabilities are less than or equal to 1 and greater than or equal to 0 . If events are mutually exclusive, then \( P(A \cup B)=P(A)+P(B) \). Therefore, if \( A \) and \( B \) are mutually exclusive, \( P(A \cup \) B \( \begin{array}{l}B)=P(A)+P(B)=0.8+0.9=1.7 \text {, which is impossible. Therefore, events } A \text { and } B \text { are not mutually exclusive. } \\ \text { D. All probabilities are less than or equal to } 1 \text { and greater than or equal to } 0 \text {. If events are mutually exclusive, } \\ \text { then } P(A \cup B)=P(A)+P(B) \text {. Therefore, if } A \text { and } B \text { are mutually exclusive, } P(A \cup \\ B)=P(A)+P(B)=0.8+0.9=1.7 \text {, which is possible. Therefore, events } A \text { and } B \text { are mutually exclusive. }\end{array} \)
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