MBNPy

Risk-informed, white-box decision-making on complex systems

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MBNPy

A Python toolkit for risk-informed, white-box decision-making on complex systems

Are you looking for a risk assessment tool that handles simultaneously multiple types of variables AND complex systems? Then MBNPy is the right tool for you!

As a companion toolkit of matrix-based Bayesian network (MBN), MBNPy is a probabilistic analysis tool specialised for large-scale, discrete-state system events. MBNPy integrates Bayesian network (BN) and System Reliability methods (SRMs) to address multiple types of variables and complex systems together.

Bayesian network

Bayesian network (BN) is a probabilistic graphical model that visualises statistical dependencies between variables. A typical BN graph for a system event is as follows:
BN is a very useful tool for probabilistic analysis of engineering systems:
  1. a complex joint probability distribution can be readily quantified by being broken down to local distributions between directly connected components; and
  2. new information can be systematically incorporated to update an entire model through observation nodes.
However, the problem arises from the converging structure between components $X_1, \cdots, X_N$ and system $S$. The relationship is defined by the $(N+1)$-dimensional distribution $P(S | X_1, \cdots, X_N)$.
When a system has 50 binary-state components, there are more than $10^{15}$ possible state combinations; if a combination can be analysed for 0.001 seconds, it takes 35,702 years to complete computation.

Matrix-based BN

MBN and its companion toolkit MBNpy solve this challenge by two approahces:
  1. It provides encoding algorithms to quantify $P(S | X_1, \cdots, X_N)$ for various classes of system events. A summary of solvable system classes is available at MBNPy's system catalogue.
  2. It provides advanced BN inference and optimisation algorithms specialised for handling large-scale systems.

Citation

For general use of MBNPy, Byun and Song (2021) can be cited. For other specific uses, a summary of developments and publications are available at Publications.

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