Probability Models

Preface<br>Arni S.R. Srinivasa Rao, Zhidong Bai and C.R. Rao<br>1. Stein’s methods<br>Qi-Man Shao and Zhuosong Zhang<br>2. Probabilities and thermodynamics third law<br>Angelo Plastino<br>3. Random Matrix Theory<br>Jeff Yao<br>4. General tools for understanding fluctuations of random variables<br>Sourav Chatterjee<br>5. An approximation scheme to compute the Fisher-Rao distance between multivariate normal distributions<br>Frank Nielsen<br>6. Chapter title to be confirmed<br>Qihua Wang<br>7. Probability Models Applied to Reliability and Availability Engineering<br>Kishor Trivedi, Kishor Trivedi and Liudong Xing<br>8. Backward stochastic differential equation– Stochastic optimization theory and viscous solution of HJB equation<br>Shige Peng<br>9. Probability Models in Machine Learning<br>Qi Meng<br>10. Chapter title to be confirmed<br>Grzegorz A. Rempala<br>11. The recursive stochastic algorithm, randomized urn models and response-adaptive randomization in clinical trials<br>Lixin Zhang<br>12. Random matrix theory: local laws and applications<br>Fan Yang, Yukun He and Zhigang Bao<br>13. KOO methods and their high-dimensional consistencies in some multivariate models<br>Y. Fujikoshi<br>14. Fourteen Lectures on Inference for Stochastic Processes<br>B.L.S. PRAKASA RAO<br>15. A multivariate cumulative damage model and some applications<br>Raul Fierro