DeepAI AI Chat
Log In Sign Up

Tractable Learning and Inference for Large-Scale Probabilistic Boolean Networks

by   Ifigeneia Apostolopoulou, et al.

Probabilistic Boolean Networks (PBNs) have been previously proposed so as to gain insights into complex dy- namical systems. However, identification of large networks and of the underlying discrete Markov Chain which describes their temporal evolution, still remains a challenge. In this paper, we introduce an equivalent representation for the PBN, the Stochastic Conjunctive Normal Form (SCNF), which paves the way to a scalable learning algorithm and helps predict long- run dynamic behavior of large-scale systems. Moreover, SCNF allows its efficient sampling so as to statistically infer multi- step transition probabilities which can provide knowledge on the activity levels of individual nodes in the long run.


Deep Reinforcement Learning for Stabilization of Large-scale Probabilistic Boolean Networks

The ability to direct a Probabilistic Boolean Network (PBN) to a desired...

Variational Probabilistic Inference and the QMR-DT Network

We describe a variational approximation method for efficient inference i...

Influence and Dynamic Behavior in Random Boolean Networks

We present a rigorous mathematical framework for analyzing dynamics of a...

Decentralized State-Dependent Markov Chain Synthesis for Swarm Guidance

This paper introduces a decentralized state-dependent Markov chain synth...

Efficient Sampling for Selecting Important Nodes in Random Network

We consider the problem of selecting important nodes in a random network...

Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model

Social dynamics is concerned primarily with interactions among individua...