infer_lcm_rw.Rdinfer_lcm_rw conduct local maximum a posteriori inference
for associative and structuallearning
infer_lcm_rw(X, opts)
| X | matrix cotaining time, US, CSs. time stimulus onset, unit is sec US Unconditioned Stimulus CS Conditioned Stimului or Context. If using multiple CS, set variables name as CS1,CS2,CS3... |
|---|---|
| opts | (optional)options used in inference a hyperparameter of beta prior(default = 1) b hyperparameter of beta prior(default = 1) c_alpha concentration parameter for Chinese restaurant process prior(default = 1) stickiness stickiness parameer for Chinese restaurant process prior(default = 0) K maximum number of latent causes(default = 10) c_alpha concentration parameter for Chinese restaurant process prior(default = 0.1) g temporal scaling parameter(default = 1) psi [N x 1]binary vector specifying when protein synthesis inhibitor is injected(default = 0) eta learning rate(default = 0.2) maxIter maximum number of iterations between each trial(default = 3) w0 initial weight value(default = 0) sr US variance(default = 0.4) sx stimulus variance(default = 1) theta response threshold(default = 0.3) lambda response gain(default = 0.005) K maximum number of latent causes(default = 15) nst If you don't want to use a nonlinear sigmoidal transformation, you set nst = 0.(default = 0) |
V: vector of conditioned response on each trial
Zp: latent cause posterior before observing US(Trial*K)
Z: latent cause posterior(Trial*K)
# results <- infer_lcm_rw(X, opts)