is a C program for simplified manifold MALA sampling of ODE model parameters. This project has a gh page: http://a-kramer.github.io/mcmc_clib/, with more detailed instructions.

install the following dependencies (libraries)

External library dependencies
	For ODE: 
		Sundials 2.4.0 or later
	For MCMC:
		GSL	1.15 or later
		CBLAS

For VFGEN:
	CLN 1.3.2 or later
	GiNaC 1.6.2 or later
	mini XML 2.6 or later

for example, on ubuntu, you can install the following packages:

libmxml-dev 
libmxml1 
libatlas-base-dev 
libatlas3gf-base
libginac-dev 
libcln-dev 
libsundials-serial-dev 
libsundials-cvode1
libsundials-cvodes2
libatlas-dev
libgsl0-dev

to satisfy these dependencies.

Then, inspect the Makefile. Once you are satisfied, type

make -B

note that the option -B will compile everything, even if the binaries appear up to date. This option is not necessary once you have compiled the binaries a first time successfully.

./ode_smmala -l ./model.so -c ./data.cfg -b \
                 -o sample.double -s ${sample_size} > ttr.out

-b 
      switches output mode to binary, otherwise text mode (printf)
      Burn-In Sample will always be printed to stdout

-c data.cfg
      configuration (data, reference data, inputs, output function,
          prior, etc.)

-l model.so
      shared library file

-s N
          sample size

-h 
          prints help

The configuration file includes the measurement time specifications, the data, the standard deviation of observations and the hyperparameters \mu and \Sigma^{-1} of the Gaussian prior. Optionally some of the sampling parameters can be set there for convenience.

The following Parameters can be set in the cfg file:

These definitions should not have spaces before the '=' sign since everything before = is checked for matches with option names (i.e.
« t0 »=0.0; is not the same as «t0»=0.0; ).

it might be helpful to specify the sample size like this in bash:

   -s $((2**14))
   -s $((10**6))

because 1e6 or 1E6 will not work. (the sample size is an integer).

====================================================================================

Output of parameters sample {p} will have the following structure (in binary and text mode; though, in binary mode there are no newlines):

sampling is done in logarithmic space, so use exp(p[i]) if you want to simulate trajectories

columns: parameters and log-posterior rows: Markov Chain members (i.e. the sample members)

   Line 
   L1   p[0] p[1] p[2] ... p[n-1] log-Posterior {\n}
   L2   p[0] p[1] p[2] ... p[n-1] log-Posterior {\n}
   L3   p[0] p[1] p[2] ... p[n-1] log-Posterior {\n}
   L4   p[0] p[1] p[2] ... p[n-1] log-Posterior {\n}
   ...
   L{sample_size}      ...

you can load the binary sample using an fread type function.

e.g. in octave: [VAL, COUNT] = fread (FID, SIZE, PRECISION,SKIP, ARCH)

so:

     FID=fopen("MySample.double","r");
     sample=fread(FID,[n+1,sample_size],"double");
     fclose(FID);

should work fine. In fact, the sources include octave scripts that read and process the sample.