Public API

Functions:

All public functions require as a first argument a pointer to amSampler structure.

int initAMSampler(amSampler *am, int nmodels, int *model_dims,

targetFunc logpost, double *initRWM);

To initialize amSampler you need to provide:

• the number of models in nmodels;
• the dimensions for each model in model_dims;
• the logarithm of the target distribution (or posterior distribution in a Bayesian analysis) in logpost (see targetFunc);
• and an array with initial conditions for each model in initRWM.

initRWM is a flattened array, with the initial values in contiguous order for each model. If initRWM is passed as NULL, then the initial values are randomly selected from a uniform distribution in [0, 1).

initAMSampler allocates necessary memory for most array in the rest of the structures.

void freeAMSampler(amSampler *am);

Once you finish working with amSampler, you should free the memory with a call to freeAMSampler.

void estimate_conditional_probs(amSampler *am, int nsweep2);

Before running RJMCMC sweeps, you need to call estimate_conditional_probs() to estimate the conditional probabilities of your models, to adjust for an appropriate proposal distribution.

The statistics collected during the estimate_conditional_probs() calls, are stored in the cpstats member inside the amSampler struct. For a full description of cpstats, see condProbStats.

void burn_samples(amSampler *am, int nburn);

It is advised to burn some samples at the begining of a RJMCMC run to let the Markov chain converge.

void rjmcmc_samples(amSampler *am, int nsweep);

This is the function that will provide the RJMCMC samples.

The statistics collected during the rjmcmc_samples() calls, are stored in the st member inside the amSampler struct. For a full description of st, see runStats.

C struct’s

runStats

Contains statistics of the rjmcmc_samples call.

naccrwmb

[unsigned long] Number of accepted Block-RWM.

ntryrwmb

[unsigned long] Number of tried Block-RWM.

naccrwms

[unsigned long] Number of accepted Single-RWM.

ntryrwms

[unsigned long] Number of tried Single-RWM.

nacctd

[unsigned long] Number of accepted Auto RJ.

ntrytd

[unsigned long] Number of tried Auto RJ.

theta_summary

[double ***] The theta parameter for each model, for each sweep. theta_summary[i][j][k] is the \(\theta_k\) component for the j-th sweep for the i-th model.

theta_summary_len

[int *] the number of sweeps for theta_summary in model k. theta_summary_len[1] = 20 means that the model 1 has 20 \(\theta\) samples.

ksummary

[int *] the number of times the model was visited.

amSampler

ch

A chainState instance containing the chain state of the RJMCMC.

jd

A proposalDist instance containing the proposal distribution for the chain.

cpstats

A condProbStats instance that holds statistics for the estimation of conditional probabilities (if ran).

st

A runStats instance that holds statistics for the RJMCMC runs (if ran).

doAdapt

A bool value indicating if Adaptation is to be performed or not during the RJMCMC runs. Default value is true.

doPerm

A bool value indicating if Permutation is to be performed or not during the RJMCMC runs. Default value is false.

student_T_dof

int type. The degree of freedom of the Student’s T distribution to sample from. If 0, sample from a Normal distribution instead. Default value is 0.

am_mixfit

A automix_mix_fit value. Default is FIGUEREIDO_MIX_FIT.

seed

An unsigned long value to initialize the random number generator. Defaults to clock time.

condProbStats

rwm_summary_len

TBD.

sig_k_rwm_summary

TBD.

nacc_ntry_rwm

TBD.

nfitmix

TBD.

fitmix_annulations

TBD.

fitmix_costfnnew

TBD.

fitmix_lpn

TBD.

fitmix_Lkk

TBD.

proposalDist

The proposal distribution is of the form:

\[p(\theta) = \sum_{k=1}^{k_{max}} L_k \exp \left(-\frac{1}{2} \left( \theta_k - \mu_k \right) B \cdot B^T \left( \theta_k - \mu_k \right) \right).\]

nmodels

The number of models in M (referred as \(K_{max}\) in thesis, p 143)

nMixComps

The numberof mixture components for each model (refferred as \(L_k\) in thesis, p144)

model_dims

The dimension of the \(\theta\) parameter space for each model.

lambda

The relative weights for each mixture component for each model. lambda[i][j] is the weight of the j-th mixture component for model \(M_i\).

mu

The mean vector for the mixture component for each model. mu[i][j][k] is the k-th vector index for the j-th mixture component for model \(M_i\).

B

\(B \cdot B^T\) is the covariance matrix for each mixture component for each model. B[i][j][k][m] is the k,m index of the B matrix for mixture component j of model \(M_i\).

sig

Vector of adapted RWM scale parameters for each model.

chainState

Struct to hold the MCMC chain state

theta

The current value of \(\theta_k\) in the chain.

pk

TBD.

log_posterior

The current value of the log-posterior.

current_model_k

The current model k in the chain.

mdim

The dimension of the current model in the chain.

current_Lkk

TBD

nreinit

TBD.

reinit

TBD.

pkllim

TBD.

doBlockRWM

A bool value to indicate if the chain should do a Block-RWM. This is done every 10 sweeps.

isBurning

A bool that indicates wether the chain is burning samples.

sweep_i

The index of the chain.

Typedef’s and Enum’s

targetFunc

targetFunc is the prototype of the log-posterior function that must be passed to amSampler upon initialization.

typedef double (*targetFunc)(int model_k, double *x);

bool

bool is a typedef for int. C does not have a bool type but int is just as good.

typedef int bool;

automix_mix_fit

Enum to specify whether using Figuereido or AutoRJ in conditional probabilities estimation.

typedef enum { FIGUEREIDO_MIX_FIT = 0, AUTORJ_MIX_FIT } automix_mix_fit;