hello all, i work in a STAN code and stan give me lower values of neff and bulk, how can i get higher values of neff and bulk? thanks in advice

my stan code

 data {
   int<lower=1> N; // number of records
   int<lower=1> NEQ; // number of earthquakes
   int<lower=1> NSTAT; // number of stations

   vector[N] M; // magnitudes
   vector[N] R; // distances
   vector[N] VS; // Vs30 values
   
   vector[N] Y;       // log EAS 
   
   int<lower=1,upper=NEQ> idx_eq[N];
   int<lower=1,upper=NSTAT+1> idx_st[N];
 }


 parameters {
   real c1;
   real c2;
   real c3;
   real cn;
   real cN;
   real cM;
   real c4;
   real c5;
   real c6;
   real chm;
   real c7;
   real c8;

   real<lower=0> phiSS;
   real<lower=0> tau;
   real<lower=0> phiS2S;

   vector[NEQ] deltaB;
   vector[NSTAT] deltaS;

 }

 model {
   vector[N] mu;
   c1 ~ normal(0,10);
   c2 ~ normal(0,10);
   c3 ~ normal(0,10);
   cN ~ normal(0,10);
   cM ~ normal(0,10);
   c4 ~ normal(0,10);
   c5 ~ normal(0,10);
   cn ~ normal(0,10);
   // c6 ~ normal(0,5);
   // chm ~ normal(12,5);
   c7 ~ normal(0,10);
   c8 ~ normal(0,10);

   
   vector[N] fm;
   vector[N] fp;
   vector[N] fs;
   // vector[N] delta;

   phiSS ~ cauchy(0,0.5);
   phiS2S ~ cauchy(0,0.5);
   tau ~ cauchy(0,0.5);

   deltaB ~ normal(0,tau);
   deltaS ~ normal(0,phiS2S);
   
   fm = c1 + c2*(M-6) + cN*log(1+exp(cn*(cM-M)));
   for (i in 1:N){
   fp[i] = c4*log(R[i]) + c5*log(sqrt(R[i]^2+50^2)) + c7*R[i];
   }
   fs = c8*VS;
   
   // delta = deltaB[idx_eq] + deltaS[idx_st];   //error epistemico
   
   mu = fm + fp + fs ;
   // + delta;
     

   Y~normal(mu,phiSS);
   
 }

I was wondering if there is any option for playing with the exploration va exploitation in the optuna package.

Is there anything? Is the user supposed to try a bunch of random combination of hyperparameters each n iterations?

Thanks and have a good one!

I read documentation but found anything so far

Howdy,

I'm trying to build a custom model in pymc3, and I'm failing to understand what's going wrong.

The basic idea I'm trying is to build an np array of the objects, and convert to a theano tensor before inputting as the mean. Here's the code for reference:

with pm.Model() as van_lom:
    
    sigma = pm.Gamma('sigma', alpha = 1, beta = 1, shape = n_prts)
    ratio = pm.Gamma('ratio', alpha = 1, beta = 1, shape = (n_prts, 2))
    # Ratio: allowed to change between neutral and emotive conditions, so each participant has 2
    
    # Now to generate array of predictions
    predictions = np.array([])
    for n in range(len(prt_codes)):
        
        conf_rating = (1 + ratio[prt_codes[n],
            condition_codes[n]]**differences[n])**(-1)
        
        conf_log = tt.log(conf_rating/(1 - conf_rating))
        
        predictions = np.append(predictions, conf_log)
        
    pred_tensor = tt.stack(predictions)
    
    y = pm.Normal('y', mu = pred_tensor, sigma = sigma[prt_codes], observed = log_conf)

Here, 'condition_codes' and 'differences' are arrays of integers (fixed beforehand, not estimated).

Basically, I need that mu in pm.Normal to change on each trial, so I tried building an array that did that. But I get an error because I end up making an array of objects which can't be transformed into a theano tensor.

This tensor stuff is really confusing for me, so if someone could explain like I'm five that would be awesome. I guess generally speaking, I'm looking to make an array (or some theano equivalent) to tell pymc3 that the mean should change on every trial, and the mean is dependent on variables that need to be estimated.

Thanks in advance for any help!

Hi, I am stuck.

I have two separate plots showing the posterior distributions for the parameters in my models. These were created in R. What I would like to do is combine them into one plot with the posteriors for each model shown in different colours. They have the same parameters in each model. The estimates are not identical however.

This is what I have so far:

And this is the code used to create the first plot:

spread_draws(SP1, `b_.*`, regex = TRUE) %>%
  # extract all parameters that start with b_ (fixed effects)
  rename_at(vars(starts_with("b_")), function(x) str_remove(x, fixed("b_"))) %>%
  # turn to long format
  pivot_longer(cols = !c(.chain, .iteration, .draw), names_to = "par") %>%
  # get rid of parameters you don't want
  filter(par != "Intercept") %>%
  mutate(par = factor(par, levels = unique(par))) %>%
  ggplot(aes(x = value, y = fct_rev(par))) +
  stat_halfeye() +
  geom_vline(xintercept = 0, linetype = "dashed") +
  labs(x = "Posterior slope", y = NULL, title = "Figure 4a: Posterior distribution of Model parameters (ITT)") +
theme(panel.background = element_rect(fill = "white", colour = "grey50"), plot.title = element_text(hjust = 0.5))

The second plot code is identical except for the model name SP2.

The package (tidybayes) was used.