Recursive estimation is a basic operation in statistical inference that may be implemented and deployed on faulty hardware with error rates governed by energy consumption. We analyze the loss in estimation performance due to noise in recursive probability computation for the binary case, and develop an optimal energy allocation strategy. Simulations show the validity of analytical bounds.