Forecast joint engineering outcome distribution

Learn a company-local partially pooled discrete Bayesian network from complete mature observations, validate it strictly out of time against an independent baseline, and answer coherent conditional joint engineering-outcome queries with exact inference and Dirichlet posterior intervals.

Sequential Bayesian & bandits Forecast CTOs Tech CEOs IT managers Team leads Investors

What it's for

Completes the site's joint-posterior promise with tenant-local calibration, explicit validation and AI-ready conditional probability output.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

Field Type Role Required
as_of_ms number ≥ 0 Your calibration Yes
current_evidence any Your calibration Yes
dependency_edges array of objects (3 fields) ≥ 0 items Evidence Yes
dirichlet_prior_strength number ≥ 0.01, ≤ 10000 Your calibration Optional
historical_observations array of objects (6 fields) Evidence Yes
holdout_fraction number ≥ 0.05, ≤ 0.5 Your calibration Optional
max_detail_rows integer ≥ 1, ≤ 500 Numerical control Optional
maximum_expected_calibration_error number ≥ 0, ≤ 1 Your calibration Optional
minimum_holdout_rows integer ≥ 1 Your calibration Optional
minimum_local_training_rows integer ≥ 1 Your calibration Optional
node_specs array of objects (3 fields) Evidence Yes
query_events array of objects (2 fields) Evidence Yes
random_seed integer ≥ 0, ≤ 4294967295 Your calibration Optional
simulation_count integer ≥ 200, ≤ 100000 Your calibration Optional
target_scope_id string non-empty Your calibration Yes

Each historical_observations record

Field Type Required
evidence_verified boolean Yes
id string (non-empty) Yes
matured_at_ms number (≥ 0) Yes
observed_at_ms number (≥ 0) Yes
scope_id string (non-empty) Yes
states object Yes
Example input 
{
  "as_of_ms": 1000,
  "current_evidence": {
    "load": "good"
  },
  "dependency_edges": [
    {
      "child_node_id": "delivery",
      "id": "forecast-edge-load-delivery",
      "parent_node_id": "load"
    },
    {
      "child_node_id": "quality",
      "id": "forecast-edge-load-quality",
      "parent_node_id": "load"
    }
  ],
  "historical_observations": [
    {
      "evidence_verified": true,
      "id": "joint-history-company-0",
      "matured_at_ms": 2,
      "observed_at_ms": 1,
      "scope_id": "company",
      "states": {
        "delivery": "bad",
        "load": "good",
        "quality": "bad"
      }
    },
    {
      "evidence_verified": true,
      "id": "joint-history-company-1",
      "matured_at_ms": 3,
      "observed_at_ms": 2,
      "scope_id": "company",
      "states": {
        "delivery": "good",
        "load": "bad",
        "quality": "good"
      }
    },
    {
      "evidence_verified": true,

Truncated for display — the full payload is 1019 lines.

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

Example output 
{
  "assumptions": [
    "Every row is a complete mature zero-inclusive aggregate observation, and the prospectively governed DAG supplies the conditional-independence structure.",
    "Company-local CPT rows shrink toward the submitted cross-scope empirical distribution; the reported interval reflects Dirichlet parameter uncertainty, not structural or causal uncertainty.",
    "Conditioning answers an observational probability query. It must not be read as the effect of intervening on an evidence node."
  ],
  "configuration": {
    "as_of_ms": 1000,
    "dirichlet_prior_strength": 8,
    "holdout_fraction": 0.2,
    "maximum_expected_calibration_error": 0.15,
    "minimum_holdout_rows": 5,
    "minimum_local_training_rows": 20,
    "random_seed": 0,
    "simulation_count": 200
  },
  "decision": "forecast_calibrated_for_conditional_use",
  "forecast": {
    "current_evidence": {
      "load": "good"
    },
    "evidence_probability": 0.5,
    "most_probable_joint_outcome_states": [
      {
        "outcome_states": {
          "delivery": "good",
          "quality": "good"
        },
        "probability": 0.4856
      },
      {
        "outcome_states": {
          "delivery": "good",
          "quality": "bad"
        },
        "probability": 0.2627
      },
      {
        "outcome_states": {
          "delivery": "bad",
          "quality": "good"
        },
        "probability": 0.1634
      },

Truncated for display — the full payload is 98 lines.

How it works

Sequential Bayesian & bandits — Learn while deciding — update beliefs as evidence arrives and choose where the next unit of effort is worth spending.

  1. 1 Validate the prospectively governed discrete DAG, complete state dictionaries, mature point-in-time rows, aggregate target scope, current evidence and requested joint outcome events.
  2. 2 Split local history by time, fit hierarchical empirical-Bayes CPTs that shrink sparse local parent rows toward cross-scope empirical frequencies, and score the frozen network against an independent-node baseline using joint log loss, Brier score and calibration error.
  3. 3 Refit through the as-of cutoff, enumerate the exact conditional joint distribution and draw Dirichlet CPT posteriors to report parameter-uncertainty intervals for each requested event.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Assumptions & guardrails

  • The likelihood or reward model, prior support, action logging, delayed outcomes, and any stationarity assumptions match the deployment process.
  • Rows are complete, mature and zero-inclusive at one aggregate grain; the DAG is frozen before validation; peer scopes are sufficiently comparable for shrinkage; and conditioning is not interpreted as intervention.
  • Posterior probability and adaptive selection are model-conditional; they are not substitutes for randomized propensities or guaranteed safety.
  • The interval covers CPT parameter uncertainty under the submitted structure, not omitted variables, structural uncertainty, causal effect or named-person outcomes.

Minimum evidence

  • historical_observations: required and organization-defined
  • node_specs: required and organization-defined
  • dependency_edges: at least 0 rows/items
  • query_events: required and organization-defined
  • target_scope_id: required and organization-defined
  • current_evidence: required and organization-defined
  • as_of_ms: required and organization-defined

How to validate it

Use chronological train/calibration/test windows, compare proper scores and decision value with a simple baseline, and recalibrate only from outcomes resolved after prediction time.

Calibrating it to your org

Same for everyone

The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.

Specific to you

  • one tenant-scoped wide state panel at a stable cadence with exact observation and outcome-maturity times, plus an immutable network definition and temporal train/holdout split that prevents future leakage
  • aggregate unit and cadence, state thresholds, outcome maturity, missingness treatment, DAG and edge semantics, peer-scope transportability, local shrinkage strength, holdout fraction, calibration gates, current evidence cutoff and query definitions

Calibration workflow

  1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
  2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
  3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
  4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
  5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
  6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own. 

gitrevio_capabilities_search
  { "q": "learn a companylocal partially pooled discrete" }
  → finds "forecast_joint_engineering_outcome_distribution"

gitrevio_capability_describe
  { "capability_id": "forecast_joint_engineering_outcome_distribution" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "forecast_joint_engineering_outcome_distribution", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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