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@jackylouise6: Bioshock infinite replay about to be peak

🇮🇹🔱Jacksinious links🐎🏛️
🇮🇹🔱Jacksinious links🐎🏛️
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Thursday 01 October 2026 03:54:21 GMT
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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


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