Réflexions d'un officier allemand, aujourd'hui confronté à ses doutes, qui s'interroge sur sa part de responsabilité dans la tragédie de la dernière guerre. …

L'histoire de la Wehrmacht par ceux qui l'ont commandée. Une somme majeure sur la Deuxième Guerre mondiale. …

Le nouveau volume de la collection "Maîtres de guerre" : Rommel, le renard du désert, et protégé d'Hitler. …

Integrated circuit technology is widely used for the full integration of electronic systems. In general, these systems are realized using digital techniques implemented in CMOS technology. The low power dissipation, high packing density, high noise immunity, ease of design and the relative ease of scaling are the driving forces of CMOS technology for digital applications. Parts of these systems cannot be implemented in the digital domain and will remain analog. In order to achieve complete system integration these analog functions are preferably integrated in the same CMOS technology. An important class of analog circuits that need to be integrated in CMOS are analog filters.

During the past decade, geneticists have cloned scores of Mendelian disease genes and constructed a rough draft of the entire human genome. The unprecedented insights into human disease and evolution offered by mapping, cloning, and sequencing will transform medicine and agriculture. This revolution depends vitally on the contributions of applied mathematicians, statisticians, and computer scientists. Mathematical and Statistical Methods for Genetic Analysis is written to equip students in the mathematical sciences to understand and model the epidemiological and experimental data encountered in genetics research. Mathematical, statistical, and computational principles relevant to this task are developed hand in hand with applications to population genetics, gene mapping, risk prediction, testing of epidemiological hypotheses, molecular evolution, and DNA sequence analysis. Many specialized topics are covered that are currently accessible only in journal articles. This second edition expands the original edition by over 100 pages and includes new material on DNA sequence analysis, diffusion processes, binding domain identification, Bayesian estimation of haplotype frequencies, case-control association studies, the gamete competition model, QTL mapping and factor analysis, the Lander-Green-Kruglyak algorithm of pedigree analysis, and codon and rate variation models in molecular phylogeny. Sprinkled throughout the chapters are many new problems.

During the past decade, geneticists have constructed detailed maps of the human genome and cloned scores of Mendelian disease genes. They now stand on the threshold of sequencing the genome in its entirety. The unprecedented insights into human disease and evolution offered by mapping and sequencing will transform medicine and agriculture. This revolution depends vitally on the contributions of applied mathematicians, statisticians, and computer scientists. Mathematical and Statistical Methods for Genetic Analysis is written to equip graduate students in the mathematical sciences to understand and model the epidemiological and experimental data encountered in genetics research. Mathematical, statistical, and computational principles relevant to this task are developed hand in hand with applications to gene mapping, risk prediction, and the testing of epidemiological hypotheses. The book includes many topics currently accessible only in journal articles, including pedigree analysis algorithms, Markov chain Monte Carlo methods, reconstruction of evolutionary trees, radiation hybrid mapping, and models of recombination. Exercise sets are included.