Alexander Melnikov is a Professor at the University of Alberta working in stochastic analysis and its applications in finance, statistics and insurance. He is the author of eight books and over one hundred papers in leading academic journals and venues. He is a Fellow of the Russian Academy of Natural Sciences, a recipient of the Leontiev medal of this academy and the McCalla Professorship of the University of Alberta. In addition to his academic engagements, he held several senior positions in business and professional organizations: Chief-Scientist at Risk-Invest Deutschland (Frankfurt), Vice-President of the Russian Society of Actuaries, Deputy Director at the Center for Actuarial and Financial Studies (Moscow), Senior Research Consultant at the Model Capital Management (Boston).

• 1 Probabilistic Foundations.- 2 Random variables and their quantitative characteristics.- 3 Expectations and convergence of sequences of random variables.- 4 Weak convergence of sequences of random variables.- 5 Absolute continuity of probability measures and conditional expectations.- 6 Discrete time stochastic analysis: basic results.- 7 Discrete time stochastic analysis: further results and applications.- 8 Elements of classical theory of stochastic processes.- 9 Stochastic differential equations, diffusion processes and their applications.- 10 General theory of stochastic processes under ”usual conditions".- 11 General theory of stochastic processes in applications.- 12 Supplementary problems.- References.- Index.

Alexander Melnikov is a Professor at the University of Alberta working in stochastic analysis and its applications in finance, statistics and insurance. He is the author of eight books and over one hundred papers in leading academic journals and venues. He is a Fellow of the Russian Academy of Natural Sciences, a recipient of the Leontiev medal of this academy and the McCalla Professorship of the University of Alberta. In addition to his academic engagements, he held several senior positions in business and professional organizations: Chief-Scientist at Risk-Invest Deutschland (Frankfurt), Vice-President of the Russian Society of Actuaries, Deputy Director at the Center for Actuarial and Financial Studies (Moscow), Senior Research Consultant at the Model Capital Management (Boston).

• 1 Probabilistic Foundations.- 2 Random variables and their quantitative characteristics.- 3 Expectations and convergence of sequences of random variables.- 4 Weak convergence of sequences of random variables.- 5 Absolute continuity of probability measures and conditional expectations.- 6 Discrete time stochastic analysis: basic results.- 7 Discrete time stochastic analysis: further results and applications.- 8 Elements of classical theory of stochastic processes.- 9 Stochastic differential equations, diffusion processes and their applications.- 10 General theory of stochastic processes under ”usual conditions".- 11 General theory of stochastic processes in applications.- 12 Supplementary problems.- References.- Index.

Alexander Melnikov is a Professor at the University of Alberta working in stochastic analysis and its applications in finance, statistics and insurance. He is the author of eight books and over one hundred papers in leading academic journals and venues. He is a Fellow of the Russian Academy of Natural Sciences, a recipient of the Leontiev medal of this academy and the McCalla Professorship of the University of Alberta. In addition to his academic engagements, he held several senior positions in business and professional organizations: Chief-Scientist at Risk-Invest Deutschland (Frankfurt), Vice-President of the Russian Society of Actuaries, Deputy Director at the Center for Actuarial and Financial Studies (Moscow), Senior Research Consultant at the Model Capital Management (Boston).

• 1 Probabilistic Foundations.- 2 Random variables and their quantitative characteristics.- 3 Expectations and convergence of sequences of random variables.- 4 Weak convergence of sequences of random variables.- 5 Absolute continuity of probability measures and conditional expectations.- 6 Discrete time stochastic analysis: basic results.- 7 Discrete time stochastic analysis: further results and applications.- 8 Elements of classical theory of stochastic processes.- 9 Stochastic differential equations, diffusion processes and their applications.- 10 General theory of stochastic processes under ”usual conditions".- 11 General theory of stochastic processes in applications.- 12 Supplementary problems.- References.- Index.