4th_ed gibbs ask

Author: samagra14

md/Gibbs-Ask.md

@@ -13,5 +13,23 @@ __function__ GIBBS-ASK(_X_, __e__, _bn_, _N_) __returns__ an estimate of __P__(_
      __N__\[_x_\] ← __N__\[_x_\] + 1 where _x_ is the value of _X_ in __x__  
  __return__ NORMALIZE(__N__)  
 
+---
+__Figure__ ?? The Gibbs sampling algorithm for approximate inference in Bayesian networks; this version cycles through the variables, but choosing variables at random also works.  
+
+---
+
+## AIMA4e
+__function__ GIBBS-ASK(_X_, __e__, _bn_, _N_) __returns__ an estimate of __P__(_X_ | __e__)
+ __local variables__: __N__, a vector of counts for each value of _X_, initially zero
+        __Z__, the nonevidence variables in _bn_
+        __x__, the current state of the network, initially copied from __e__
+
+ initialize __x__ with random values for the variables in __Z__
+ __for__ _j_ = 1 to _N_ __do__
+&emsp;&emsp;&emsp;__choose__ any variable _Z<sub>i</sub>_ from __Z__ acoording to any distribution _&rho;(i)_
+&emsp;&emsp;&emsp;&emsp;&emsp;set the value of _Z<sub>i</sub>_ in __x__ by sampling from __P__(_Z<sub>i</sub>_ &vert; _mb_(_Z<sub>i</sub>_))
+&emsp;&emsp;&emsp;&emsp;&emsp;__N__\[_x_\] &larr; __N__\[_x_\] &plus; 1 where _x_ is the value of _X_ in __x__
+&emsp;__return__ NORMALIZE(__N__)
+
 ---
 __Figure__ ?? The Gibbs sampling algorithm for approximate inference in Bayesian networks; this version cycles through the variables, but choosing variables at random also works.