📊 Conditional Probability - Bayes' Theorem 🎲

Welcome to another essential lecture in our Statistics and Probability 101 series! In this session, we will explore Bayes' Theorem, a fundamental principle in conditional probability. Whether you're new to probability or aiming to deepen your understanding, this lecture is designed to equip you with the tools to calculate conditional probabilities effectively and apply Bayes' Theorem in various real-world scenarios.
✨ What You Will Learn:
🔍 Understanding Bayes' Theorem
Definition and Significance
What is Bayes' Theorem and why it's crucial in probability theory.
Historical context and real-world applications of Bayes' Theorem.
🧮 Calculating Conditional Probabilities with Bayes' Theorem
Step-by-Step Numerical Examples
Detailed walkthroughs of problems using Bayes' Theorem.
Applying the theorem to calculate posterior probabilities.
📐 Practical Applications of Bayes' Theorem
Medical Diagnostics
Calculating the probability of a disease given a positive test result.
Spam Filtering
Determining the likelihood that an email is spam based on certain keywords.
Decision Making and Risk Assessment
Using Bayes' Theorem to make informed decisions under uncertainty.
🔗 Connecting Bayes' Theorem with Other Probability Concepts
Relationship with Conditional Probability
How Bayes' Theorem builds upon the principles of conditional probability.
Integration with the Law of Total Probability
Enhancing probability calculations by combining Bayes' Theorem with other fundamental laws.
💡 Enhancing Analytical Skills
Problem-Solving Strategies
Developing techniques to approach and solve complex probability problems using Bayes' Theorem.
Real-World Case Studies
Analyzing comprehensive case studies to see Bayes' Theorem in action across different fields.
📚 References:
Probability and Statistics for Engineering and the Sciences by JAY L. DEVORE
Understanding Boxplots
Statistics for Engineers and Scientists, Fourth Edition by William Navidi, Colorado School of Mines
Khan Academy
Join us in this comprehensive lecture to master Bayes' Theorem in Conditional Probability. By the end of this session, you'll be proficient in applying Bayes' Theorem to calculate conditional probabilities, analyze complex probabilistic events, and make informed decisions based on robust statistical reasoning! 🚀
#Probability #Statistics #BayesTheorem #ConditionalProbability #LearnProbability #ProbabilityTheory #DataAnalysis #DecisionMaking #EngineeringStatistics #DataScience #Mathematics #ProbabilityCalculations #StatisticalMethods #BayesianInference

[@fatima_ahmed06]

بارك الله فيك يا دكتور
الفهم نعمة و الله✨..

[@ElhosseiniAcademy]

وفيكم ...اهلا وسهلا بكم ...شاركونا الأجر وادعمونا بالنشر

[@EsraaWanis]

مشكور جدا يادكتور
🇱🇾👍

[@ElhosseiniAcademy]

اهلا وسهلا بكم

[@ao3093]

ممكن تنزل شرح للي قلته في نهاية الفديو Application to reliability

[@shimaarafat4663]

تحية طيبة لحضرتك دكتور مصطفى
حضرتك قولت فى الدقيقة 2.06 إن تقاطع A and B هو تقاطع B andA هنا سؤالى إزاى التقاطعين متساويين على حسب القوانين اللى مكتوبه فى الدقيقة 2.06
وشكرا مقدما

[@ZozZoz-zv5oh]

أستاذ شلون عرفت
P(ND)=0.995
ما انطة شيء عنها

[@ElhosseiniAcademy]

drive.google.com/file/d/1VFM1d2RfX3tk42pyrgWq3eKReKjb4oTu/view?usp=sharing

[@عبدالرحمنطارق-و4ط]

الناتج للعملية في الدقيقة 18:40 هو 0.082

[@testforall555]

شكرا جزيلا لحضرتك من شخص تخصصه بيولوجي بس بيموت في الحساب والكمبيوتر وبيحاول يفهم اساسيات ومصارين اللوغاريتمات في تخصص المعلوماتية الحياتية. الحمد لله شوفت فيدوهات كتيرة بالانجليزي بس واضح جدا ان شرح حضرتك بيسهل كتييييير في توصيل المعلومة - يا بخت بصراحة متخصصين الرياضيات - معلش عارف انه ممكن يكون طلب مستحيل او مش مكانه بس لو ينفع فيديو قصير لاساسيات حسابات الاحتمالات مع ان حضرتك وضحت ضمنيا في فيديوهاتك كتير منها وربنا يوفقك ويوفق الجميع. وهل ممكن فيديو عن (hidden markov model) او لو حضرتك عملته يا ريت تدينا الرابط. كل دي داخلة في التنبأ بتركيب البروتينات او ايجاد علاقة في التطور بين التسلسل الوراثي بين الكائنات وغيرها كتير ومعلش للإطالة وشكرا. محمد

[@ElhosseiniAcademy]

اهلا وسهلا بحضرتك.... ان شاء الله... دعواتك... ادعمونا بالنشر وشاركونا الاجر