What Bayesian networks are ideal for:

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Question restatement: What Bayesian networks are ideal for?

Option A: Machine vision. While Bayesian networks can be used in some vision tasks to model uncertainties and perform probabilistic reasoning, machine vision is more commonly associated with deterministic feature extraction and deep learning approaches (e.g., convolutional neural networks). This makes A not the best fit as a general characterization of what Bayesian networks are ideal for. The reasoning flaw here is treating BN as a primary tool for vision tasks without acknowledging its role in uncertain inference, which is supplementary rather than the core strength in most standard vision pipelines.

Option B: Describing linear regression models. Linear regression is a simple, well-understood statistical method that makes strong assumptions about linearity and Gaussian noise. Bayesian networks, on the other hand, are graphical models that encode conditional dependencies among many variables and are particularly suited for reasoning under uncertainty and multiple interacting causes. The mismatch is that B frames BN as describing a single, closed-form regression, which misses the broader inferential capabilities of BN beyond standard regression. This makes B an incorrect depiction of what BNs are ideal for.

Option C: Predicting the likelihood that any one of several possible known causes was the contributing factor for an event. This option aligns with the core strength of Bayesian networks: modeling probabilistic relationships among multiple potential causes and updating beliefs as evidence comes in. BNs are designed to compute posterior probabilities for different causes given observed data, handle uncertainty, and reason about which cause is most likely among a set of known alternatives. A practical flaw in the other options is their narrower scope or misalignment with the probabilistic, multi-cause reasoning that BN structures support.

In summary, option C correctly captures the primary use case of Bayesian networks: probabilistic reasoning about which among several known factors contributed to an event, with updated likelihoods as new evidence is observed. The other choices either overstate their relevance to BN (A) or mischaracterize the typical applications and strengths of Bayesian networks (B).

BL40A2060 Internet of Things – Introduction - Online teaching 1.8.2024-30.7.2025 Quiz 3 – Probability and Game Theory

What Bayesian networks are ideal for:

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BL40A2060 Internet of Things – Introduction - Online teaching 1.8.2024-30.7.2025 Quiz 3 – Probability and Game Theory

What Bayesian networks are ideal for:

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