(no subject)

[Kuppa Kiran Kumar]

Hi All,
I thank Celine for her feed back  one my  query. Like
fire feeds on fire, it seems my queries are feeding on
themselves recursively -:). On again i request to 
help  me out of the myraid of questions i am besotted
with.
Thanking you,

Kuppa Kiran

_______________________________________________________
Here is my  understanding of MCMC. Monte carlo is an
integration technique which random numbers to find the
integration intervals and sum it up to arrive at the
final integral. And markov chains are memory-less
processes ( a physical phenomenonm which depends only
on the state which it was previously existing). In a
MCMC computatation, states depicting the physical
parameters are generated randomly according to a
probability distribution and the resulting
mathematical complexities in integration and
summations are handled by monte carlo integration.

Now, how does this translate into the software McSim
is the question which i am unable to resolve
completely. Though the example of PharmocoKinetics
(PBPK) is provided, it falls out of my  domain of
study; though i have really made a honest atempt to
understand it, i failed to get the physical feel of
the simulation. Other examples provided with the
software, though understandable, do not have a
physical  basis - hence for me, the process of
understanding remains in complete. One- because of
lack of domain knowledge and the othe- because of lack
of  physical situations to back up the model and input
files.

 I understand the difficulty involved in resolving
this issue, in a more generic fashion. Hence i raise
few specific questions which i request to  be
adressed.

Model File:

As i  understand, States are such set of variables
which have dynamics.This dynamics can be expressed in
form of a first  order differential equations ( this
what McSim supports). The physical  system evolves
according to these set of differential equations.The
variables that we want to  monitor ( which are
functions of other parameters and state variables) are
specified in CalcOutputs section which are called at
time intervals specified in Print () specification. 
Inputs (as i  understand) is a special section in
which prior distributions for variables can be
specified. Correct me if i am worng.

Simulation File:

Via, Simulation() i  can specify what sort of
simulation do  i want to perform.
I have some problem in getting the utility of
Integrate() specification. Say i perform MCMC
simulation- as i know,monte carlo technique is used to
integrate; what is this lsodes?. And where are these
markov chains genreated ? and according to what
distribution function are these states generated ?
Where did i specify this? (Is this done in Distrib()
specification ?- if so what is the ratonale for
specifying in Simulation rather than model).

The MCMC section says that these simulaions are used
in context of bayesian inference. I have done quite an
extensive study on bayesian models and the idea that i
have is- the bayesian model has quite a number of
random variables in hierarchy of nodes. The dependency
among these nodes is provided by the conditional
probabilities of the node , given its parents. I
assign priors on some of the variables and then
provide observed / experimental data so that i  can
query the bayesian network and get the required
posterior distributions. Period(!). I am done with the
simulation.
My problem is where exactly is this hierarchy
specified ? I see that Level has something to  do with
this - but again things are not clear because the
explicit mention of conditional probabilities is not
visible anywhere through my  eyes. I request you to 
help me here also.

Experiment:

I have no clue what does this experiment section
addresses (!). Also the document does not clearly tell
what  are the issues that  need to  be considered in
each section.

The clear seperation of model and simulation
conditions does sound a good design of the software.
Also the seperation of things like " output varables
to  be monitored", "print specification" etc are very
convenient- in sense that it enables one to think of
the problem domain quite clearly.

Here i am sending a sort of generic hierarchial model,
which i contrive ( i hope this is a workable model).I
would be really glad if you could guide me in
simulating using McSim software.
_______________________________________________________



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Bayesian-examole.doc
Description: Bayesian-examole.doc