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Random variables and expectation
Distributions, expected value, variance, linearity of expectation.
A random variable is a number attached to each outcome; its expectation is the probability-weighted average, and expectation is linear even when the variables are dependent. Picture it: the distribution as a bar chart, the mean as its balance point. Think it: expectation is an integral against the probability measure.
कार्यरत उदाहरण · 10 * 1/2
चरणानुक्रमे
- 10 \cdot 1 \cdot \frac{1}{2} = 5
Multiply: 10·1(1/2) = 5.
उत्तर उघडा
आता तुम्ही प्रश्न निवडा, किंवा स्वतःचा प्रश्न टाइप करा किंवा रेखाटून दाखवा. प्रत्येक टप्प्यात, चित्रे आहेत, तुम्ही विचारण्याआधी उत्तर लपवलेले आहे.
मोफत खाते प्रत्येक शिकवणी वर टिपण्णी जोडते, तुम्ही पूर्ण केलेल्या गोष्टीचा नोंदवही, तुमच्या सोडविलेल्या समस्या एका ठिकाणी, आणि या पानाच्या विषयी तुम्ही विचारू शकता अशा शिक्षकाला. गणित स्वतःच सर्वांसाठी खुले आहे, दाखलन झालेले असो किंवा नसो.
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कसे कराल: Random variables and expectation
- Multiply: 10·1(1/2) = 5.
लोक विचारतात ते प्रश्न
What is the difference between probability and statistics?
Probability goes from a known model to what the data should look like; statistics goes from data back to the model. Probability theory is the deductive half.
What does the law of large numbers promise?
That the average of many independent samples converges to the expected value. It says nothing about any single trial.
अधिक माहिती Probability Theory
Sample spaces and the axiomsThe common distributionsThe law of large numbers and the central limit theorem