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bayes_algorithm [2016/10/26 22:44]
nikolaj
bayes_algorithm [2016/10/29 16:33]
nikolaj
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 | @#55CCEE: context ​    | @#55CCEE: $W_z:X\to {\mathbb R}$ | | @#55CCEE: context ​    | @#55CCEE: $W_z:X\to {\mathbb R}$ |
 | @#FF9944: definition ​ | @#FF9944: $\Gamma: (X\to {\mathbb R})\to X\to {\mathbb R}$ | | @#FF9944: definition ​ | @#FF9944: $\Gamma: (X\to {\mathbb R})\to X\to {\mathbb R}$ |
-| @#FF9944: definition ​ | @#FF9944: $bel_{\mathrm out}[bel_{\mathrm in}](x) := N^*W_z(x)\int_A K_u(x,​x'​)\,​bel_{\mathrm in}(x'​){\mathrm d}$ |+| @#FF9944: definition ​ | @#FF9944: $bel_{\mathrm out}[bel_{\mathrm in}](x) := N^*W_z(x)\int_A K_u(x,​x'​)\,​bel_{\mathrm in}(x'​){\mathrm d}x'$ | 
 + 
 +>this is the algorithm for the case where all the ingredient have these types. In practice, Coming up with an initial $bel$ is a also part of the task. 
 +>$N^*$ is supposed to be the normalization of the whole term on the right of it - a normalization to the sum/​integral of $bel_{\mathrm in}$. In practice, the latter should normalize to $1$.
  
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 === Discussion === === Discussion ===
-$N^*$ is supposed to be the normalization of the whole term on the right of it - a normalization to the sum/​integral of $bel_{\mathrm in}$. In practice, the latter should normalize to $1$. 
  
 $K_u(x,​x'​)$ ought to capture the propagation,​ possibly determined by actions $u$. $K_u(x,​x'​)$ ought to capture the propagation,​ possibly determined by actions $u$.
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 and you apply the Bayes Filter with and you apply the Bayes Filter with
  
-P(x_k, x_{k-1}) = p( x_k \mid   ​x_{k-1}) = \mathcal{N} ( {F}_k x_{k-1} + B_k u_k,  {Q}_k) $+P_u(x_k, x_{k-1}) = p( x_k \mid   ​x_{k-1}, u_k) = \mathcal{N} ( {F}_k x_{k-1} + B_k u_k,  {Q}_k) $
  
-O(x_k) = p( {z}_k\mid ​ x_k) = \mathcal{N}( {H}_{k} x_k,  {R}_k) $+O_z(x_k) = p( {z}_k\mid ​ x_k) = \mathcal{N}( {H}_{k} x_k,  {R}_k) $
  
 where $ \mathcal{N}( x, \sigma^2) $ is the normal distribution,​ except of course with multivariate arguments. where $ \mathcal{N}( x, \sigma^2) $ is the normal distribution,​ except of course with multivariate arguments.
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