THE PURPOSE OF LARGE SCALE STRUCTURE (LSS) SURVEYS BOTH SPACE BASED SUCH AS EUCLID AND WFIRST AND GROUND BASED SUCH AS DESI AND LSST IS TO ELUCIDATE THE QUESTIONS OF ORIGINS OF STRUCTURE IN OUR UNIVERSE AND ITS CONTENTS. THE PRIMARY GOAL OF THIS PROPOSAL IS TO DEVELOP A NEW THEORETICAL AND NUMERICAL MODELING OF HOW TO EXTRACT THE COSMOLOGICAL INFORMATION FROM THE LARGE SCALE STRUCTURE OBSERVATIONS BY THESE SURVEYS. WE PROPOSE TO DO THIS IN THE CONTEXT OF A RECENTLY DEVELOPED FRAMEWORK WHICH HOLDS THE PROMISE OF EXTRACTING ALL THE COSMOLOGICAL INFORMATION OPTIMALLY. THE METHOD STARTS WITH AN EXACT BAYESIAN PROBABILISTIC MODEL FOR THE COSMOLOGICAL PARAMETERS GIVEN THE DATA WHICH IS THEN SOLVED THROUGH A PROCESS THAT COMBINES ELEMENTS OF NUMERICAL OPTIMIZATION IN HIGH DIMENSIONS AND ANALYTIC MARGINALIZATION TO FIND THE BEST SOLUTION FOR COSMOLOGICAL PARAMETERS AND THEIR COVARIANCE MATRIX. WE PROPOSE TO APPLY THIS METHOD BOTH TO WEAK LENSING AND GALAXY CLUSTERING OBSERVATIONS AS WELL AS TO DATASETS THAT COMBINE BOTH. WE WILL INVESTIGATE COSMOLOGICAL PARAMETERS AND ASTROPHYSICAL NUISANCE PARAMETERS THAT ARE TO BE MARGINALIZED OVER. THE METHODS WILL BE DEVELOPED USING REALISTIC SIMULATIONS OF BOTH DARK MATTER AND OF GALAXIES POPULATED IN THE DARK MATTER SIMULATIONS BEFORE BEING APPLIED TO REAL DATA. RESULTS OF THIS STUDY WILL PROVIDE NEW METHODS THAT MAY DRAMATICALLY IMPROVE UPON THE EXISTING METHODS AND MAY BE OF DIRECT AND SIGNIFICANT BENEFIT TO SPACE AND GROUND BASED LSS MISSIONS.

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