Bayesian inference

A Bayesian inference device for performing Bayesian inference in a Bayesian network, which includes a continuous child node and a discrete parent node of the child node, the discrete parent node having two states, wherein the Bayesian inference device comprises:

• a computer;
• and
• a non-transitory storage medium storing instructions that, when executed by the computer, cause the computer to:
• provide continuous probability distributions of observations of the child node for the two states of the parent node, respectively, based on values of observations of the child node for which the states of the parent node are known, wherein the probability distributions comprise a lower-located probability distribution at a relatively lower location with respect to the values of the observations of the child node and a higher-located probability distribution at a relatively higher location with respect to the values of the observations of the child node;
• and
• infer for a new observation of the child node, for which the state of the parent node is not known, a probability of at least a first state of the two states of the parent node, wherein the inferring includes calculating (i) for the lower-located probability distribution, a mass of a portion of the lower-located probability distribution that is larger than a value of the new observation of the child node and (ii) for the higher-located probability distribution, a mass of a portion of the richer-located probability distribution that is smaller than the value of the new observation of the child node, and calculating the probability of the first state based on the calculated masses.

A non-transitory computer readable medium storing instructions for performing Bayesian inference in a Bayesian network, which includes a continuous child node and a discrete parent node of the child node, the discrete parent node having two states, wherein when executed by a computer, the instructions cause the computer to:

• provide continuous probability distributions of observations of the child node for the two states of the parent node, respectively, based on values of observations of the child node for which the states of the parent node are known, wherein the probability distributions comprise a lower-located probability distribution at a relatively lower location with respect to the values of the observations of the child node and a higher-located probability distribution at a relatively higher location with respect to the values of the observations of the child node;
• and
• infer for a new observation of the child node, for which the state of the parent node is not known, a probability of at least a first state of the two states of the parent node, wherein the probability of at least the first state is inferred by calculating (i) a mass of a portion of the lower-located probability distribution that is larger than a value of the new observation of the child node and (ii) a mass of a portion of the higher-located probability distribution that is smaller than the value of the new observation of the child node, and calculating the probability of the first state based on the calculated masses.