Methods and systems for constructing Bayesian belief networks

A user interface for users of a belief network including a child node Y and one or more parent nodes Xi (i=1, . . . , n) for the child node Y,

• wherein the user interface is configured to display the child node Y, the parent nodes Xi, and one or more links that indicate relationships between the child node Y and the parent nodes Xi;
• wherein the user interface is configured to allow the users of the belief network to indicate a desired strength of relationships between the child node Y and the parent nodes Xi by adjusting corresponding values on the links between the child node Y and the parent nodes Xi;
• wherein the child node Y is one of:
• a discrete Boolean node having states true and false;
• a discrete Ordinal node having a plurality of ordered states;
• and a Categorical node having a plurality of unordered states;
• and
• wherein each one of the parent nodes Xi (i=1, . . . , n) is one of:
• a discrete Boolean node having states true and false;
• a discrete Ordinal node having a plurality of ordered states;
• and a Categorical node having a plurality of unordered states;
• wherein the user-adjusted values on the links represent parameters of a causal influence model that describes influence of the parent nodes Xi on possible states of the child node Y;
• and
• wherein the user interface is further configured to provide the parameters of the causal influence model to a processing system that converts the parameters into entries of a conditional probability table;
• wherein the influence of the parent nodes Xi on possible states of the child node Y comprise at least one of a promoting influence and an inhibiting influence;
• wherein said each of the parent nodes Xi has a promoting influence on the child node Y when a true state or a higher level state of the parent node increases the probability of the child node being in the true state or the higher level state;
• and
• wherein said each of the parent nodes Xi has an inhibiting influence on the child node Y when a false state or a lower level state of the parent node increases the probability of the child node being in the true state or the higher level state.

A method of computing a belief in a belief network including a child node Y and one or more parent nodes Xi (i=1, . . . , n) for the child node Y, the method comprising:

• assuming that the child node Y and each one of the parent nodes Xi (i=1, . . . , n) are one of:
• a discrete Boolean node having states true and false, a discrete Ordinal node having a plurality of ordered states, and a discrete Categorical node having a plurality of unordered states;
• assuming that the influence of each parent node Xi on the child node Y is one of a promoting influence and an inhibiting influence;
• receiving as input one or more parameters of a causal influence model that describes influence of the parent nodes Xi on possible states of the child node Y;
• converting the parameters of the causal influence model into one or more entries of a conditional probability table that provides a probability distribution for all the possible states of the child node Y, for each combination of possible states of the parent nodes Xi;
• wherein the parameters of the causal influence model comprise at least one of:
• a baseline probability bk (k=1, . . . , m) for each state yk (k=1, . . . , m) of the child node Y, wherein the baseline probability bk represents a probability that the child node Y is in the state yk when none of the parent nodes Xi (i=1, . . . , n) exert influence on the child node Y;
• and
• a causal influence hijk (i=1 . . . n;
• j=1 . . . mi;
• k=1 . . . m), wherein the causal influence hijk, represents a change in the probability of the state yk (k=1, . . . , m) of the child node Y, when the i-th parent node Xi (i=1, . . . , n) is in a state xij (j=1, . . . , mi).

A computer-readable non-transitory storage medium having stored therein computer-usable instructions for a processing system, wherein said instructions when executed by the processing system causes the processing system to:

• assume that the child node Y and each one of the parent nodes Xi (i=1, . . . , n) are one of:
• a discrete Boolean node having states true and false, a discrete Ordinal node having a plurality of ordered states, and a Categorical node having a plurality of unordered states;
• assume that the influence of each parent node Xi on the child node Y is one of a promoting influence and an inhibiting influence
• receive as input one or more parameters of a causal influence model that describes influence of one or more parent nodes Xi (i=1, . . . , n) in a belief network on possible states of a child node Y in the belief network;
• and
• convert the parameters of the causal influence model into one or more entries of a conditional probability table that provides a probability distribution for all the possible states of the child node Y, for each combination of possible states of the parent nodes Xi(i=1, . . . , n);
• wherein the parameters of the causal influence model comprise at least one of:
• a baseline probability bk (k=1, . . . , m) for each state yk (k=1, . . . , m) of the child node Y, wherein the baseline probability bk represents a probability that the child node Y is in the state yk when none of the parent nodes Xi (i=1, . . . , n) exert influence on the child node Y;
• and
• a causal influence hijk (i=1 . . . n;
• j=1 . . . mi;
• k=1 . . . m), wherein the causal influence hijk represents a change in the probability of the state yk (k=1, . . . , m) of the child node Y, when the i-th parent node Xi (i=1, . . . , n) is in a state xij (j=1, . . . , mi).