1. Home
  2. Detail

@user90781370771707: 🫶🥰❤️#اليمن_صنعاء_تعز_اب_ذمار_عدن_وطن_واحد

مزاج أنثى ❤️🥰🫶
مزاج أنثى ❤️🥰🫶
Open In TikTok:
Region: YE
Monday 14 September 2026 18:24:38 GMT
754
56
1
0

Music

Download

No Watermark .mp4 (1.5MB) No Watermark(HD) .mp4 (1.5MB) Watermark .mp4 (0MB) Music .mp3

Comments

user7044962980058
الذكي :
🌹🌹🌹
2026-09-14 18:31:10
0
To see more videos from user @user90781370771707, please go to the Tikwm homepage.

Other Videos

#CapCut فكرة جديده #capcut #capcutpioneer #fyp #pioneertemplate
#CapCut فكرة جديده #capcut #capcutpioneer #fyp #pioneertemplate
🤣😂😂😂😂😂😂😂😂 African Boy Crying Then Laughing Meme 😂😂😂😂😂😂😂 #meme #memes #funny #funnyvideos #viral
🤣😂😂😂😂😂😂😂😂 African Boy Crying Then Laughing Meme 😂😂😂😂😂😂😂 #meme #memes #funny #funnyvideos #viral
وَاللَّهُ يُحِبُّ الصَّابِرِينَ ١٤٦ سورة آل عمران : ياسر الدوسري _____________________ #قران_كريم #تلاوة_خاشعة #سورة_ال_عمران #ياسر_الدوسري #كرومات_جاهزة_لتصميم #كرومات_قرآنیة #quran_kareem #quran #lslam
وَاللَّهُ يُحِبُّ الصَّابِرِينَ ١٤٦ سورة آل عمران : ياسر الدوسري _____________________ #قران_كريم #تلاوة_خاشعة #سورة_ال_عمران #ياسر_الدوسري #كرومات_جاهزة_لتصميم #كرومات_قرآنیة #quran_kareem #quran #lslam
Title:
Title: "Simplifying Bayes' Theorem: A Practical Example" (The Circle 11.11 Series) #nolieism Introduction: Bayes' Theorem is a fundamental concept in probability theory and statistics that allows us to update our beliefs about an event based on new evidence. It's often used in various fields, including machine learning, medical diagnostics, and more. Let's break it down into a simplified formula and explore a real-life example to understand its practical application. The Formula: Bayes' Theorem can be expressed as: P(A|B) = \frac{P(B|A) \cdot P(A)}{P(B)}P(A∣B)=P(B)P(B∣A)⋅P(A)​ Where: P(A|B)P(A∣B) is the probability of event A given that event B has occurred. P(B|A)P(B∣A) is the probability of event B given that event A has occurred. P(A)P(A) is the prior probability of event A. P(B)P(B) is the prior probability of event B. The Applied Example: Let's consider a medical example. Suppose you are undergoing a cancer screening test. P(A)P(A) represents the prior probability of having cancer in the general population, which is relatively low. P(B|A)P(B∣A) is the probability that the test is positive given that you actually have cancer. P(B)P(B) is the probability of getting a positive test result, regardless of whether you have cancer or not. Suppose: P(A) = 0.01P(A)=0.01 (1% of the population has cancer). P(B|A) = 0.9P(B∣A)=0.9 (the test is 90% accurate in detecting cancer). P(B) = 0.05P(B)=0.05 (5% of all people will get a positive test result). Now, you can use Bayes' Theorem to calculate the probability that you have cancer given a positive test result: P(A|B) = \frac{0.9 \cdot 0.01}{0.05} = 0.18P(A∣B)=0.050.9⋅0.01​=0.18 This means that even with a positive test result, there's an 18% chance that you actually have cancer. It demonstrates how our initial beliefs (prior probability) can be updated with new evidence (test result). Conclusion: Bayes' Theorem is a powerful tool for updating probabilities based on new information. It's widely applicable in decision-making and problem-solving, allowing us to make more informed choices in various domains. #By Sir NolieBoy Rama Bantanos (The Circle 11.11 Series) #sir #tiktokindia #arithmetic #mathematics #research #study #Science #TikTok #By #nolieism #The
Baat to sai h 😂😂 #trending #viral #tiktok #fyp #followme
Baat to sai h 😂😂 #trending #viral #tiktok #fyp #followme
#CapCut #tiktok #status #edit #story
#CapCut #tiktok #status #edit #story

About

Legal