AMMCMC

Purpose

AMMCMC extends MCMC with adaptive covariance updates.

Full Sampling Section Keys

• Sampling.Method (required): must be AMMCMC.
• Sampling.Variables (required, array):
• name (required)
• description (required)
• distribution.type (required)
• distribution.parameters (required)
• runtime-safe parameter sets:
  • Flat: min, max
  • Log: min, max
  • Normal: mean, stddev
  • Log-Normal: mean, stddev
  • Logit: location, scale
• Sampling.LogLikelihood (required): {name, expression} list.
• Sampling.selection (optional, string)
• Sampling.Bounds:
• Base keys (required):
  • num_chains (integer)
  • num_iters (integer)
  • proposal_scale (number)
• Adaptive keys (optional):
  • adapt_enabled (boolean, default true)
  • adapt_start_iter (integer, default 100)
  • adapt_window (integer, default 25)
  • adapt_eps (number, default 1e-6)
  • adapt_scale (number, default 2.38)

Full Skeleton

Sampling:
  Method: "AMMCMC"
  Variables:
    - name: p1
      description: parameter 1
      distribution:
        type: Flat
        parameters:
          min: -5
          max: 5
  LogLikelihood:
    - name: L_total
      expression: "-0.5*(p1/1.0)^2"
  Bounds:
    num_chains: 6
    num_iters: 8000
    proposal_scale: 0.12
    adapt_enabled: true
    adapt_start_iter: 200
    adapt_window: 50
    adapt_eps: 1.0e-6
    adapt_scale: 2.38

Example

Sampling:
  Method: "AMMCMC"
  Variables:
    - name: m0
      description: scalar mass
      distribution:
        type: Flat
        parameters:
          min: 100
          max: 3000
  LogLikelihood:
    - name: L_total
      expression: "L_higgs + L_dm"
  Bounds:
    num_chains: 6
    num_iters: 8000
    proposal_scale: 0.12
    adapt_enabled: true
    adapt_start_iter: 200
    adapt_window: 50
    adapt_eps: 1.0e-6
    adapt_scale: 2.38