000			02345nam a2200289Ia 4500
003			OSt
005			20250716115727.0
008			220909b |||||||| |||| 00| 0 eng d
020			_a9783642162176
040			_aCSL _beng _cCSL
041			_aeng
084			_aB28 Q1 TB _qCSL
100			_aBaragona, Roberto _eauthor. _9815713
245		0	_aEvolutionary Statistical Procedures _b: Evolutionary Computation Approach to Statistical Procedures Designs and Applications
260			_aLondon : _bSpringer, _c2011.
300			_axi, 276p.
490			_aStatistics and Computing
500			_aIncludes References 261-272p.; Index 273-276p.
520			_aThis proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statistical techniques. Modern statistical research is on the threshold of solving increasingly complex problems in high dimensions, and the generalization of its methodology to parameters whose estimators do not follow mathematically simple distributions is underway. Many of these challenges involve optimizing functions for which analytic solutions are infeasible. Evolutionary algorithms represent a powerful and easily understood means of approximating the optimum value in a variety of settings. The proposed text seeks to guide readers through the crucial issues of optimization problems in statistical settings and the implementation of tailored methods (including both stand-alone evolutionary algorithms and hybrid crosses of these procedures with standard statistical algorithms like Metropolis-Hastings) in a variety of applications. This book would serve as an excellent reference work for statistical researchers at an advanced graduate level or beyond, particularly those with a strong background in computer science.
650			_aComputational approach. _9815714
650			_aStatistical proceedures. _9815715
650			_aMathematics.
700			_aBattaglia, Francesco _eco-author. _9815716
700			_aPoli, Irene _eco-author. _9815717
942			_hB28, Q1 TB _cTEXL _2CC _n0
999			_c9020 _d9020