Constructing a Bayesian Network involves several steps:
1. Define the Variables: Identify the key variables relevant to the nanotechnology application you are studying. For example, in nanoparticle synthesis, variables could include temperature, reactant concentration, and particle size.
2. Structure the Network: Arrange the variables to form a directed acyclic graph, representing the conditional dependencies among them. This requires domain expertise to ensure the structure accurately reflects the real-world relationships.
3. Parameter Learning: Use data to estimate the conditional probability distributions of each variable given its parents in the network. This can be done using methods such as Maximum Likelihood Estimation or Bayesian Estimation.
4. Inference: Once the network is constructed and parameters are learned, you can perform inference to answer queries about the system. For example, you could predict the probability of achieving a desired nanoparticle size given certain synthesis conditions.
