hssm.likelihoods

Likelihood functions and distributions that use them.

hssm.likelihoods.logp_ddm

logp_ddm(data: ndarray, v: float, a: float, z: float, t: float, err: float = 1e-15, k_terms: int = 20, epsilon: float = 1e-15) -> ndarray

Compute analytical likelihood for the DDM model with sv.

Computes the log-likelihood of the drift diffusion model f(t|v,a,z) using the method and implementation of Navarro & Fuss, 2009.

Parameters:

• data (ndarray) –

  data: 2-column numpy array of (response time, response)

• v (float) –

  Mean drift rate. (-inf, inf).

• a (float) –

  Value of decision upper bound. (0, inf).

• z (float) –

  Normalized decision starting point. (0, 1).

• t (float) –

  Non-decision time [0, inf).

• err (float, default: 1e-15 ) –

  Error bound.

• k_terms (int, default: 20 ) –

  number of terms to use to approximate the PDF.

• epsilon (float, default: 1e-15 ) –

  A small positive number to prevent division by zero or taking the log of zero.

Returns:

• ndarray –

  The analytical likelihoods for DDM.

hssm.likelihoods.logp_ddm_bbox

logp_ddm_bbox(data: ndarray, v, a, z, t) -> ndarray

Compute blackbox log-likelihoods for ddm models.

hssm.likelihoods.logp_ddm_sdv

logp_ddm_sdv(data: ndarray, v: float, a: float, z: float, t: float, sv: float, err: float = 1e-15, k_terms: int = 20, epsilon: float = 1e-15) -> ndarray

Compute the log-likelihood of the drift diffusion model f(t|v,a,z).

Using the method and implementation of Navarro & Fuss, 2009.

Parameters:

• data (ndarray) –

  2-column numpy array of (response time, response)

• v (float) –

  Mean drift rate. (-inf, inf).

• a (float) –

  Value of decision upper bound. (0, inf).

• z (float) –

  Normalized decision starting point. (0, 1).

• t (float) –

  Non-decision time [0, inf).

• sv (float) –

  Standard deviation of the drift rate [0, inf).

• err (float, default: 1e-15 ) –

  Error bound.

• k_terms (int, default: 20 ) –

  number of terms to use to approximate the PDF.

• epsilon (float, default: 1e-15 ) –

  A small positive number to prevent division by zero or taking the log of zero.

Returns:

• ndarray –

  The log likelihood of the drift diffusion model with the standard deviation of sv.

hssm.likelihoods.logp_ddm_sdv_bbox

logp_ddm_sdv_bbox(data: ndarray, v, a, z, t, sv) -> ndarray

Compute blackbox log-likelihoods for ddm_sdv models.

hssm.likelihoods.logp_full_ddm

logp_full_ddm(data: ndarray, v, a, z, t, sz, sv, st)

Compute blackbox log-likelihoods for full_ddm models.