Calculate Maximum Entropy Distribution

Computes the maximum entropy probability distribution subject to given moment constraints. The maximum entropy principle selects the least informative distribution consistent with constraints, avoiding unwarranted assumptions. Widely used in portfolio optimization, risk-neutral pricing, and statistical inference.

Use Cases:

• Portfolio optimization with return/variance constraints
• Risk-neutral density estimation from option prices
• Bayesian prior selection with limited information
• Robust probability modeling
• Market-implied distribution inference

Formula: Maximize H(P) = -Σ p_i log(p_i) subject to Σ f_k(i) p_i = μ_k

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

POST /quantlib/physics/max-entropy

cURL

mean_constraint

curl --request POST \ --url https://api.fincept.in/quantlib/physics/max-entropy \ --header 'Content-Type: application/json' \ --header 'X-API-Key: <api-key>' \ --data ' { "n_states": 5, "constraint_values": [ 0.15, 0.2, 0.3, 0.2, 0.15 ] } '

200

{
  "success": true,
  "data": {
    "distribution": [
      0.15,
      0.2,
      0.3,
      0.2,
      0.15
    ],
    "n_states": 5
  }
}

Authorizations

X-API-Key · string · header · required

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

Body

application/json

n_states · integer · required

Number of states in the distribution

Example:

5

constraint_values · number[] · required

Target probabilities for each state (constraints). Each value represents the desired probability for the corresponding state index.

Example:

[0.1, 0.2, 0.3, 0.25, 0.15]

Response

200

application/json

Maximum entropy distribution computed successfully

success · boolean

Example:

true

data · object

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