# Covariance Uncertainty Propagation

> Covariance Uncertainty Propagation is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.013/call, status unknown (last checked 2026-09-14).

Propagates declared correlated measurement uncertainty through 1–8 sensitivities using the quadratic form c^T C c, returning exact variance and a rational square-root enclosure.

## Facts

- Endpoint: POST https://www.mahastrategies.com/api/v1/micro/covariance-uncertainty
- Price: $0.013/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/covariance-uncertainty-propagation-c698c41d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_sjyPwS9o72WEbcCyxkvbS

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability covariance-uncertainty-propagation-c698c41d -d '<json body>'
```

Example prompt: Propagate the correlated measurement uncertainty for my 3-sensitivity vector [0.5, 1.2, -0.8] through a 3×3 symmetric positive-semidefinite covariance matrix [[0.01,0.002,0],[0.002,0.04,0.001],[0,0.001,0.009]] using public data, and give me the exact variance and rational square-root enclosure in meters.

## When to prefer this

Choose this endpoint when you need a mathematically rigorous, first-order correlated uncertainty propagation with an exact rational square-root enclosure (not just a floating-point approximation) for 1–8 sensitivities. It is ideal when your covariance matrix is known exactly (declared, not empirically estimated) and you need certified interval bounds rather than Monte Carlo or empirical estimates. Prefer this over generic statistics libraries when auditability (via input/receipt digests) and exact arithmetic enclosures are required for metrology, calibration, or governance reporting workflows.

## Known failure modes

- Covariance matrix not symmetric or not positive-semidefinite — request rejected with validation error
- Sensitivity array or covariance dimensions mismatched — schema validation failure
- More than 8 sensitivities provided — exceeds maxItems constraint
- Output unit string missing or too long (>32 chars) — validation error
- Payment of 13000 base units USDC not received — x402 payment required error
- Data class not 'public' or 'synthetic' — enum validation failure

## How this service works

Propagate declared correlated measurement uncertainty for 1-8 sensitivities using c-transpose C c. Requires exact symmetric positive-semidefinite covariance; returns exact variance and a rational square-root enclosure. First-order only; no empirical covariance validation.

## Output

Returns a JSON object with version, offerId (covariance-uncertainty), amountBaseUnits (13000), an inputDigest and receiptDigest for auditability, a result object containing the exact computed variance and rational square-root enclosure, and a boundaries array (up to 128 elements) representing interval enclosure data.

## Request schema (JSON Schema)

```json
{
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "properties": {
  "input": {
   "type": "object",
   "properties": {
    "type": {
     "type": "string",
     "const": "http"
    },
    "method": {
     "type": "string",
     "enum": [
      "POST"
     ]
    },
    "bodyType": {
     "type": "string",
     "enum": [
      "json",
      "form-data",
      "text"
     ]
    },
    "body": {
     "type": "object",
     "properties": {
      "dataClass": {
       "type": "string",
       "enum": [
        "public",
        "synthetic"
       ]
      },
      "sensitivities": {
       "type": "array",
       "minItems": 1,
       "maxItems": 8
      },
      "covariance": {
       "type": "array",
       "minItems": 1,
       "maxItems": 8
      },
      "outputUnit": {
       "type": "string",
       "minLength": 1,
       "maxLength": 32
      }
     },
     "required": [
      "dataClass",
      "sensitivities",
      "covariance",
      "outputUnit"
     ],
     "additionalProperties": false
    }
   },
   "required": [
    "type",
    "method",
    "bodyType",
    "body"
   ],
   "additionalProperties": false
  },
  "output": {
   "type": "object",
   "properties": {
    "type": {
     "type": "string"
    },
    "example": {
     "type": "object",
     "properties": {
      "version": {
       "type": "string",
       "enum": [
        "maha-microproducts/0.1"
       ]
      },
      "offerId": {
       "type": "string",
       "enum": [
        "covariance-uncertainty"
       ]
      },
      "amountBaseUnits": {
       "type": "string",
       "enum": [
        "13000"
       ]
      },
      "inputDigest": {
       "type": "string",
       "maxLength": 71
      },
      "result": {
       "type": "object"
      },
      "boundaries": {
       "type": "array",
       "minItems": 0,
       "maxItems": 128
      },
      "receiptDigest": {
       "type": "string",
       "maxLength": 71
      }
     },
     "required": [
      "version",
      "offerId",
      "amountBaseUnits",
      "inputDigest",
      "result",
      "boundaries",
      "receiptDigest"
     ],
     "additionalProperties": false
    }
   },
   "required": [
    "type"
   ]
  }
 },
 "required": [
  "input"
 ]
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/covariance-uncertainty-propagation-c698c41d/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from www.mahastrategies.com](https://www.zero.xyz/host/www.mahastrategies.com/llms.txt)