pymc3 horseshoe prior implementation

pymc3-horseshoe-prior.py

def horseshoe_prior(name, X, y, m, v, s):
    '''
    Regularizing horseshoe prior as introduced by Piironen & Vehtari
    https://arxiv.org/pdf/1707.01694.pdf
    
    name: variable name
    X: X (2-d array)
    y: y (for setting pseudo-variance)
    m: expected number of relevant features (must be < total N)
    v: regularizing student-t df
    s: regularizing student-t sd
    '''
    
    half_v = v/2
    n = X.shape[0]
    u = np.mean(y)
    M = X.shape[1]
    
    # Estimate binomial psuedo variance using gaussian approximation 
    sigma = np.sqrt(1/u * (1-u)/1) # from https://arxiv.org/pdf/1707.01694.pdf p.15
    
    tau0 = (m/(M-m)) * (sigma/np.math.sqrt(n))
    tau_t = pm.HalfCauchy(f"tauT_{name}", beta = 1)
    tau = tau0*tau_t

    c2_t = pm.InverseGamma(f"c2_{name}", half_v, half_v)
    c2 = np.power(s,2) * c2_t

    lambda_m = pm.HalfCauchy(f"lambdaM_{name}", beta = 1, shape = M)
    lambda_t = (pm.math.sqrt(c2)*lambda_m) / pm.math.sqrt(c2 + pm.math.sqr(tau) * pm.math.sqr(lambda_m))
    

    beta_t = pm.Normal(f"betaT_{name}", mu=0, sd = 1, shape= M)
    beta = tau * lambda_t * beta_t
    
    return(beta)

Hi! I was reading the paper about the regularized horseshoe prior and came upon this gist. I was wondering if you had any idea about the v and s parameters if the priors were defined ? The paper does not seem to point at possible values if I read it correctly.

Thanks!