Systems and methods for event assignment of dynamically changing islands

A system for training a machine learning model, comprising:

• a processor;
• and
• a memory device coupled to the processor and including instructions that, when executed by the processor, configure the processor to perform operations comprising:
• receiving an island-level event data describing an island-level event occurring at an island that is capable of dynamically changing by splitting or merging with another island, wherein:
• the island is a part of a network comprising a plurality of islands including the another island and comprises one or more individual components;
• and
• the island-level event data fails to immediately identify a source individual component of the island-level event from the one or more individual components;
• estimating a prior probability of each of the one or more individual components causing the island-level event;
• performing a posterior inference, using the machine learning model and based on the island-level event data and the prior probability of each of the one or more individual components, to determine a probability estimate of each of the one or more individual components causing the island-level event;
• updating the prior probability of each of the one or more individual components based on a result of the performance of the posterior inference;
• and
• executing the receiving the island-level event data, the performing the posterior inference, and the updating the prior probability repeatedly until a difference between the updated and estimated prior probability of each of the one or more individual components is less than a threshold difference, wherein:
• after the executing, the machine learning model is trained to receive the island-level event data describing the island-level event occurring at the island, and identify the source individual component as a cause of the island-level event based on the received island-level event data as the island is dynamically changing.

A non-transitory event assignment computer-readable medium (CRM) in which a program is stored for causing a computer to perform operations comprising:

• receiving an island-level event data describing an island-level event occurring at an island that is capable of dynamically changing by splitting or merging with another island, wherein:
• the island is a part of a network comprising a plurality of islands including the another island and comprises one or more individual components;
• and
• the island-level event data fails to immediately identify a source individual component of the island-level event from the one or more individual components;
• estimating a prior probability of each of the one or more individual components causing the island-level event;
• performing a posterior inference, using a machine learning model and based on the island-level event data and the prior probability of each of the one or more individual components, to determine a probability estimate of each of the one or more individual components causing the island-level event;
• updating the prior probability of each of the one or more individual components based on a result of the performance of the posterior inference;
• and
• executing the receiving the island-level event data, the performing the posterior inference, and the updating the prior probability repeatedly until a difference between the updated and estimated prior probability of each of the one or more individual components is less than a threshold difference, wherein:
• after the executing, the machine learning model is trained to receive the island-level event data describing the island-level event occurring at the island, and identify the source individual component as a cause of the island-level event based on the received island-level event data as the island is dynamically changing.