### Elements of Bayesian Networks

To introduce the elements of a Bayesian network an example from animal production is used

A livestock producer has to establish as quickly as possibly, whether an animal, which has been mated, has become pregnant. If the animal is not pregnant, he has to observe the animal with extra care, to discover, when the animal returns to oestrus. If the animal is pregnant, the farmer has to adjust the feeding, to secure fulfilment of the requirements of the embryos. In addition, in dairy production the farmer will know the remaining length of the current lactation period, and can make actions e.g. in relation to milk quota. In pig production, the farmer should ensure that there is sufficient space for the sow when it is to give birth (to farrow), either in a recently cleaned pen or in an available farrowing hut.

Therefore, a pregnancy-test is usually made. The test is similar to pregnancy test of women. There are several different methods for making the test. The methods include hormone-measures in blood or urine and ultrasound-scannings.

This setup can easily be modelled using Bayesian networks. In the figure below, a network is shown that represents the pregnancy-test scenario. The purpose of the network is to tell the farmer how probable it is that the sow or the cow is pregnant, when he knows the outcome of the pregnancy test.

### Elements of the Bayesian network

Bayesian networks are based on so-called graphical models. The networks can model very complicated systems even though they are build using very simple building blocks. The simple structure has the advantage that the models are easy to understand. The buildings blocks are presented below. We refers to the example in the figure.

**Graph ** Even though the network is simple, it illustrates many of the building blocks in Bayesian Networks. Thus, the ellipses in the figure are so-called *nodes * in the graph. A set of different states is assigned to each node. The node *State * has the states *Not Pregnant* and * Pregnant*. Similarly, the node *Test * has the states * Positive* and *Negative * assigned, corresponding to the outcome of the pregnancy test. A line connects the two nodes, a so-called *edge*. The arrow indicates the direction of the edge.

A node with incoming arrows from other nodes is a *child* of these nodes, and these are *parents * to the node. The node *State * is the only parent of the node * Test,*, which is a child of *State*. The direction of the edge indicates a causal relation. The outcome of the pregnancy test is a consequence of the state of the animal and not vice versa.

**Probability ** In addition the the graph, you have to specify the probabilities that the nodes are in different states. With respect to nodes without parents (such as *State *) the probability of each state is specified directly, e.g. based on expert knowledge or previous observations. As an example, approximately 85% of sows becomes pregnant at each mating, while the corresponding value for cows is somewhat lower (approx. 50%). In a graphical model for pregnancy testing of a sow, you would therefore specify the probability 0.15 for the state *Not Pregnant * and 0.85 for the state *Pregnant*.

For nodes with parents (such as *Test *) the probability of each state is specified for the situation where the states of the parents are known. In our example, the probability of a positive test outcome should be specified for the case when the animal is pregnant, as well as the case where she is not pregnant. Typical values for these probabilities are known from other studies of pregnancy tests. The test is positive in 95% of the cases, when the sow is pregnant. If the sow is not pregnant the probability of a positive test outcome is only 30%. Of course these probabilities should be modified the reflect the precision of the specific method used for pregnancy testing.

### Use of the network

When the graph and the probabilities are specified the network is ready for use. A pig producer performs a pregnancy test with a negative outcome and want to know the true state the sow. Thus he is interested in making a deduction in the opposite direction of arrow, that is from the child *Test * to the parent *State *. During the specification of the network we followed the direction of the arrow. Due to Bayes's formula it is possible to 'reverse' the calculations and make deductions in the opposite directions.

The Bayesian Network performs these calculations. Even with a negative outcome of the test, the probability of pregnancy is 29%. If the test outcome is positive this probability increases to approximately 95%.

This is based on the Danish Bedre Beslutninger med Bayesianske Net