riso.belief_nets.BeliefNetwork rho-alone
{
	riso.belief_nets.Variable rho
	{
		type continuous
		% distribution riso.distributions.Gaussian { mean 0.5 std-deviation 0.1 }
		distribution riso.distributions.GaussianDelta { support-point { 0.5 } }
	}

	riso.belief_nets.Variable rho-shadow
	{
		type continuous
		parents { rho }
		distribution riso.distributions.Identity { }
	}
}

riso.belief_nets.BeliefNetwork sigma-alone
{
	riso.belief_nets.Variable sigma
	{
		type continuous
		% distribution riso.distributions.Gaussian { mean 0.5 std-deviation 0.1 }
		distribution riso.distributions.GaussianDelta { support-point { 1 } }
	}

	riso.belief_nets.Variable sigma-shadow
	{
		type continuous
		parents { sigma }
		distribution riso.distributions.Identity { }
	}
}

riso.belief_nets.TemporalBeliefNetwork learn-ar-w-sensor
{
	riso.belief_nets.BeliefNetwork learn-ar-w-sensor
	{
		riso.belief_nets.Variable X
		{
			type continuous
			parents { rho-alone.rho-shadow sigma-alone.sigma-shadow prev[X] }
			parent-prior prev[X] riso.distributions.Gaussian { mean 0 std-deviation 50 } 
			distribution riso.distributions.AR1 { }
		}

		riso.belief_nets.Variable X-status
		{
			type discrete { "OK" "not OK" }
			distribution riso.distributions.Discrete { dimensions { 2 } probabilities { 0.99 0.01 } }
		}

		riso.belief_nets.Variable X-observed
		{
			type continuous
			parents { X X-status }
			distribution riso.distributions.IndexedDistribution
			{
				index-variables { X-status }
				components
				{
					% component[0]
					riso.distributions.ConditionalGaussian
					{
						conditional-mean-multiplier { 2.0 }
						conditional-mean-offset { -20.0 }
						conditional-variance { 7.0 }
					}
					% component[1]
					riso.distributions.Gaussian { mean 50.0  std-deviation 30.0 }
				}
			}
		}
	}
}