1. Home
  2. Detail

@yaquelindaariascc: AMOR REBELDE❤️‍🔥- CUSCO #amorrebelde🍻🍻😄🎶🎶🎶 #mixportuamor🎶💔 #cusco_peru🇵🇪 #fypviralシ

Yaque_🩶
Yaque_🩶
Open In TikTok:
Region: PE
Thursday 01 October 2026 13:19:19 GMT
28895
603
3
56

Music

Download

No Watermark .mp4 (18.33MB) No Watermark(HD) .mp4 (18.33MB) Watermark .mp4 (20.17MB) Music .mp3

Comments

zare.arias.22
Z A RE 💅 :
salu x ellos aunque mal pague 🍻🍻🍻 amigaaaa
2026-10-01 14:03:35
3
To see more videos from user @yaquelindaariascc, please go to the Tikwm homepage.

Other Videos

Probability distributions describe how probabilities are assigned to possible values of a random variable. There are two fundamental types you should know. 👇 1️⃣ DISCRETE DISTRIBUTION 🔢 A discrete random variable takes countable values, often integers. Examples: 🎲 Number on a dice 👥 Number of customers 📞 Number of calls 🪙 Number of heads PMF Probability Mass Function (PMF) gives the probability of each individual value. Example: 🎲 P(X = 3) = 1/6 Common discrete distributions: 🔹 Binomial 🔹 Poisson 🔹 Bernoulli 🔹 Geometric 2️⃣ CONTINUOUS DISTRIBUTION 📈 A continuous random variable can take any value within a range of real numbers. Examples: 📏 Height ⚖️ Weight 🌡️ Temperature ⏱️ Time 💰 Measurements PDF Probability Density Function (PDF) describes the density of probability across values. For continuous variables: P(X = exact value) = 0 Instead, probability is calculated over an interval. Example: P(170 < Height < 180) Common continuous distributions: 🔔 Normal ⏳ Exponential 📊 Uniform 🎯 Student’s t 🧠 EASY WAY TO REMEMBER PMF → Points / individual values PDF → Density across a continuous range 🔢 Discrete: “How likely is this exact outcome?” 📈 Continuous: “How much probability lies within this range?” 💡 Understanding probability distributions is essential for statistics, hypothesis testing, Machine Learning, and Data Science. 📌 Save this cheat sheet for your ML journey. #Probability #Statistics #DataScience #MachineLearning #creatorsearchinsights
Probability distributions describe how probabilities are assigned to possible values of a random variable. There are two fundamental types you should know. 👇 1️⃣ DISCRETE DISTRIBUTION 🔢 A discrete random variable takes countable values, often integers. Examples: 🎲 Number on a dice 👥 Number of customers 📞 Number of calls 🪙 Number of heads PMF Probability Mass Function (PMF) gives the probability of each individual value. Example: 🎲 P(X = 3) = 1/6 Common discrete distributions: 🔹 Binomial 🔹 Poisson 🔹 Bernoulli 🔹 Geometric 2️⃣ CONTINUOUS DISTRIBUTION 📈 A continuous random variable can take any value within a range of real numbers. Examples: 📏 Height ⚖️ Weight 🌡️ Temperature ⏱️ Time 💰 Measurements PDF Probability Density Function (PDF) describes the density of probability across values. For continuous variables: P(X = exact value) = 0 Instead, probability is calculated over an interval. Example: P(170 < Height < 180) Common continuous distributions: 🔔 Normal ⏳ Exponential 📊 Uniform 🎯 Student’s t 🧠 EASY WAY TO REMEMBER PMF → Points / individual values PDF → Density across a continuous range 🔢 Discrete: “How likely is this exact outcome?” 📈 Continuous: “How much probability lies within this range?” 💡 Understanding probability distributions is essential for statistics, hypothesis testing, Machine Learning, and Data Science. 📌 Save this cheat sheet for your ML journey. #Probability #Statistics #DataScience #MachineLearning #creatorsearchinsights


Flip through Little Shop by @Hikaru with me 💖🫶🏼🌸 #coloringbook #coloring #hikarukotz #coloringbookforadults #tiktokshop
Flip through Little Shop by @Hikaru with me 💖🫶🏼🌸 #coloringbook #coloring #hikarukotz #coloringbookforadults #tiktokshop
#whaneverwherever #fypシ゚viral #غمگین #عاشقانه #fypシ゚viral
#whaneverwherever #fypシ゚viral #غمگین #عاشقانه #fypシ゚viral
One step at a time
One step at a time


About

Legal