# Parameter Count

## Introduction

Displays the total number of parameters in the network, as well as a breakdown by node.

This tool can be used to identify nodes which have a large number of parameters, which may be undesirable.

For a discrete distribution (Table), the number of parameters for each parent combination will be 1 less than the number of variable states (or product of variable state counts, when a node has multiple variables). This is because a discrete probability distribution sums to 1.0, so one parameter is redundant.

## Columns

- Name - the name of the node
- Parameter count - the number of parameters in the node distribution
- Temporal order - identifies the temporal distribution (DBN nodes only)
- Related node - identifies the noisy node distribution (Noisy nodes only)

## Many parameters

A large number of parameters can be a problem if:

- It causes performance problems during inference
- If manually eliciting parameters, there are too many to gather expert opinion for.
- A distribution is being learned from data, and a larger table means it is more likely that there is insufficient data to have confidence in some parameters.

Techniques like **Divorcing**, Noisy nodes, or Latent Variables can be used to reduce complexity.

Parameter Count can calculate the number of parameters required by a node's distribution(s) even if the distribution has not been created yet.

## Memory and performance

During inference, distributions are created internally, in order to calculate the queries requested. These distributions may be larger than the original node distributions (unless the network is a tree). There are also other considerations during inference, such as which evidence is set, and which queries are requested.

See Tree Query for more information.