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analysis:course-w16:week10 [2017/01/05 15:16]
mvdm [Bayesian decoding]
analysis:course-w16:week10 [2018/07/07 10:19] (current)
Line 275: Line 275:
  
 \[P(\mathbf{n}|\mathbf{x}) = \prod_{i = 1}^{N} \frac{(\tau f_i(\mathbf{x}))^{n_i}}{n_i!} \[P(\mathbf{n}|\mathbf{x}) = \prod_{i = 1}^{N} \frac{(\tau f_i(\mathbf{x}))^{n_i}}{n_i!}
-e^{-\tau f_i (x)}\]+e^{-\tau f_i (\mathbf{x})}\]
  
 An analogy here is simply to ask: if the probability of a coin coming up heads is $0.5$, what is the probability of two coints, flipped simultaneously,​ coming up heads? If the coins are independent then this is simply $0.5*0.5$. An analogy here is simply to ask: if the probability of a coin coming up heads is $0.5$, what is the probability of two coints, flipped simultaneously,​ coming up heads? If the coins are independent then this is simply $0.5*0.5$.
analysis/course-w16/week10.txt ยท Last modified: 2018/07/07 10:19 (external edit)