Calculate Mutual Information

Calculates the mutual information I(X;Y) between two random variables, measuring the reduction in uncertainty of one variable due to knowledge of the other. Mutual information is symmetric, always non-negative, and captures both linear and non-linear dependencies, making it superior to correlation for detecting complex relationships.

Use Cases:

• Detect non-linear dependencies between assets
• Feature selection for predictive models
• Measure information flow between markets
• Quantify predictive relationships
• Portfolio diversification analysis

Formula: I(X;Y) = H(X) + H(Y) - H(X,Y) = H(X) - H(X|Y)

Credits: 5 credits per request (Pro Tier) [Tier: ENTERPRISE, Credits: 10]

POST /quantlib/physics/entropy/mutual-information

cURL

strong_dependency

curl --request POST \
  --url https://api.fincept.in/quantlib/physics/entropy/mutual-information \
  --header 'Content-Type: application/json' \
  --header 'X-API-Key: <api-key>' \
  --data '
{
  "joint_prob": [
    [
      0.45,
      0.05,
      0
    ],
    [
      0.05,
      0.4,
      0.05
    ],
    [
      0,
      0,
      0
    ]
  ],
  "base": 2.718281828459045
}
'

200

{
  "success": true,
  "data": {
    "mutual_information": 0.5623
  }
}

Authorizations

X-API-Key · string · header · required

API key for authentication. Get your key at https://api.fincept.in/auth/register

Body

application/json

joint_prob · number[][] · required

Joint probability matrix (must sum to 1)

Example:

[
  [0.4, 0.1, 0.05],
  [0.05, 0.3, 0.05],
  [0.02, 0.01, 0.02]
]

base · number · default: 2.718281828459045

Logarithm base for information calculation

Example:

2.718281828459045

Response

200

application/json

Mutual information calculated successfully

success · boolean

Example:

true

data · object

Show child attributes