Causal artificial intelligence (AI) · Model guide

Discrete dynamic Bayesian networks

Dynamic Bayesian network (DBN)

Turn a stream of noisy alerts into an updated state estimate and test how a modeled action changes later risk.

Reuse a discrete dependency model across time to update beliefs as observations arrive.

Also known as: DBN, HMM.

The model in pictures

Two time panels repeat Patient state, Vital-sign alert and Care escalation. A dashed arrow links patient state across rounds.

Early Warning for Patient Deterioration

Dynamic Bayesian network (DBN)

A Bayesian network repeated over time. It relates today's uncertain state to the next one, so a new observation can update your estimate of what is happening.

What does an abnormal vital-sign alert tell us about the patient's current condition?

The two panels are successive ward rounds, not two patients. Solid arrows connect variables within a round; the dashed arrow carries patient state into the next round. The highlighted alert is entered evidence, not a diagnosis.

Synthetic example, not real patient, customer or operational data.

Variables and symbols

Patient state
The modeled condition: stable, deteriorating or critical.
Vital-sign alert
The observed warning signal: normal or abnormal.
Care escalation
The care category: routine or escalated.
t, lag, Obs
t is the round index. Lag 0 means the same round; lag 1 means the next. Obs means observation.
Two visits show Hidden disease stage linked across time with Assay result and Symptom report observation nodes below it.

Hidden Disease Progression from Noisy Tests

Hidden Markov model (HMM)

A time-based probability model whose underlying state cannot be observed directly. Instead, imperfect measurements provide clues about that hidden state.

Which disease stage best explains a sequence of assay results and symptom reports?

Each panel is a visit. The hidden disease state continues between visits and supplies the two observation channels within each visit. Highlighted test and symptom nodes mark entered evidence, not certain knowledge of the disease stage.

Synthetic example, not real patient, customer or operational data.

Variables and symbols

Hidden disease stage
The unobserved category: remission, active or advanced.
Assay result
An imperfect test with a negative or positive result.
Symptom report
An imperfect report of mild or severe symptoms.
t, lag, Obs
t indexes visits; lag 0 is within a visit and lag 1 is between visits. Obs means observation.

When to use it

Track deterioration risk from noisy ward observations over successive rounds.

Applications

  • Equipment monitoring: update failure-state probabilities as inspections arrive.
  • Service operations: track incident progression and compare a modeled response at a chosen time.

Why it matters

The model links today’s evidence to tomorrow’s state, while its causal query modes let you compare a declared action with merely observing the same event.

Questions you can ask

  • What is the likely current state after the latest alert?
  • How does the future state distribution change if an action is set at this time step?
  • Given the observed sequence, what would the model predict if that action had differed?

Causal boundary

Supports interventions under the model’s declared structural assumptions.

Counterfactual queries additionally rely on the declared shared-noise model. These answers do not establish that the supplied causal assumptions hold in the real world.

Profile and resource limits

Stationary first-order finite-state templates; horizon, interface width, and factor growth are bounded. HMM is a profile within this family.

Online uses protected execution with plan and request limits. Check the returned method, exactness, and resource diagnostics for each query.

Existing workspace example

Early Warning for Patient Deterioration

Follow patient state, vital-sign alerts, and care escalation across ward rounds. Enter an abnormal alert at a time slice and compare exact filtering with particle inference.

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Use it in your application

Released through the native engine and host software development kits (SDKs). Online and SDK resource profiles differ; check the documentation bundled with your exact SDK version.

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