Transcription of Conditional Joint Distributions - Stanford University
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Conditional Joint Distributions - Stanford University
Conditional Joint DistributionsChris PiechCS109, Stanford UniversityTracking in 2D Space? Joint Random VariablesUse a Joint table, density function or CDF to solve probability questionUse and find independenceof random variablesThink about conditionalprobabilities with Joint variables (which might be continuous)What happens when you addrandom variables?Use and find expectationof random variablesJoint Random VariablesUse a Joint table, density function or CDF to solve probability questionUse and find independenceof random variablesThink about conditionalprobabilities with Joint variables (which might be continuous)What happens when you addrandom variables?Use and find expectationof random variablesJoint Probability TableRoommates2 RoomDblShared + Joint Random Variables0yx900900A Joint probability density functiongives the relative likelihood of more than one continuous random variable each taking on a specific value.
Gaussian Blur In image processing, a Gaussian blur is the result of blurring an image by a Gaussian function. It is a widely used effect in graphics software, typically to reduce image noise. Gaussian blurring with StDev= 3, is based on a joint probability distribution: f X,Y (x,y)= 1 2⇡ · 32 e x2+y2 2·32 F X,Y (x,y)= ⇣ x 3 ⌘ · ⇣ y 3 ...
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