Iterated conditional modes (ICM) is an algorithm used in probabilistic graphical models, specifically in the context of Markov random fields (MRFs). The goal of the algorithm is to find a local maximum of the probability distribution over the MRF variables, known as the maximum a posteriori (MAP) estimate.

To understand how the ICM algorithm works, let’s consider the following example. Suppose we have a MRF with four variables, A, B, C, and D. Each variable can take on either a value of 0 or 1. The ICM algorithm begins by initializing each variable to a random value. In the following steps, the algorithm iteratively updates each variable in turn, using the following formula:

A_new = argmax_{A} P(A|B,C,D)

In other words, the new value of variable A is chosen to be the value that maximizes the probability of A given the current values of B, C, and D. This process is repeated for each variable, until a local maximum is reached.

One key advantage of the ICM algorithm is its ability to handle MRFs with arbitrary connectivity patterns, i.e. the variables do not have to be connected in a particular way. This makes it useful for many applications, such as image segmentation and object detection.

Another example of the ICM algorithm is in the context of protein folding. In this case, the variables represent the different amino acids in a protein, and the value of each variable corresponds to the conformation of the corresponding amino acid. The ICM algorithm can be used to find the most probable conformations of the amino acids, given the known interactions between them.

Overall, the ICM algorithm is a powerful tool for solving probabilistic inference problems in graphical models, and has applications in many fields, including computer vision, machine learning, and computational biology.