STOCHASTIC PROCESSES ONLINE Videos, LECTURE NOTES AND BOOKS

TABLE OF CONTENTS

• Stochastic Models course. 26 videos. Bob Cooper.
  https://www.youtube.com/watch?v=MAgBAw-4Z1s&list=PL59NBu6N8dUqNN2S92wsA_V94jG0ujlrU
• Stochastic Processes - I (8 weeks) (MOOC Online) (18 July 2016 - 9 September 2016)
  http://nptel.ac.in/courses/111102096/
• Stochastic Processes, Video course, NPTEL Phase II
  http://nptel.ac.in/courses/111102098/
• Probability Theory and Applications by Prof. Prabha Sharma,Department of Mathematics,IIT Kanpur. 2015.
  https://www.youtube.com/playlist?list=PLbMVogVj5nJQWowhOG0-K-yI-bwRRmm3C
• Firas Rassoul-Agha. MATH 7880: Applied Stochastics. 2020. U. Utah.
  https://www.math.utah.edu/~firas/7880/Lectures/
• Michel van Biezen. Markov Chains. 39 videos of 7 minutes each. Nice.
  https://www.youtube.com/playlist?list=PLX2gX-ftPVXWgcF0WATMDr-AfvfaYjJZ3
• Markov chains. by Normalized Nerd. Good. 7 videos.
  https://www.youtube.com/playlist?list=PLM8wYQRetTxBkdvBtz-gw8b9lcVkdXQKV
• Stochastic Processes-Markov Chain. Saeideh Fallah Fini. 14 videos.
  https://www.youtube.com/playlist?list=PLmTO3lWcV3JlcYW7ekZ2m9wvxfcFppJOV
• Branching Processes. 7 videos.
  https://www.youtube.com/playlist?list=PLRogqfr-vZMfvcLjjmXnb0wk6MPTzhph-
• Markov chain. PattrickJMT. 9 videos.
  https://www.youtube.com/watch?v=uvYTGEZQTEs&list=PLANMHOrJaFxPMQCMYcYqwOCYlreFswAKP

1. STOCHASTIC PROCESSES ONLINE LECTURE NOTES. links collected by M. Hlynka, for course U Windsor Stat 4410/8410.
   https://web2.uwindsor.ca/math/hlynka/stochnotes.html
2. Virtual Laboratories in Probability and Statistics. University of Alabama - Huntsville.
   I really like this site. Take a look. There is a wealth of information here. Congratulations to those who prepared this site. Brilliant.
   http://www.math.uah.edu/stat/index.html
3. Patrik ALBIN. 2010. MSF200/MVE330 Stochastic Processes. Goteborg, Sweden. 74 pp.
   http://www.math.chalmers.se/Stat/Grundutb/GU/MSF200/S10/notes.pdf
4. David ALDOUS and James FILL. 1999. Reversible Markov Chains and Random Walks on Graphs
   http://www.stat.berkeley.edu/~aldous/RWG/book.html
5. David F. Anderson. 2017. Lecture Notes on Stochastic Processes with Applications in Biology. 217 pp.
   https://u.cs.biu.ac.il/~amirgi/SBA.pdf
6. Robert B. ASH. 2008. Basic Probability Theory
   http://www.math.uiuc.edu/~r-ash/BPT.html
7. Lothar BREUER. 2007. Introduction to Stochastic Processes. 84 pp.
   http://www.kent.ac.uk/ims/personal/lb209/files/sp07.pdf
8. Luc DEVROYE. 1986. Non Uniform Random Variate Generation, Springer Verlag. Over 600 pp.
   http://www.nrbook.com/devroye/
9. Bruce K. DRIVER. 2008. Math 180C (Introduction to Probability) Notes. U. of California, San Diego.
   http://www.math.ucsd.edu/~bdriver/180C-Spring2008/Lecture%20Notes/180Notes20080606.pdf
10. Renato FERES. Washington University. St. Louis. Math 450 - Topics in Applied Mathematics. Random Processes. 2007
    http://www.math.wustl.edu/~feres/Math450syll.html
11. SC: Rachel FEWSTER. STATS 325. University of Auckland.
    https://www.stat.auckland.ac.nz/~fewster/325/notes.php
    
12. Christopher GENOVESE. 2006. 36-703 INTERMEDIATE PROBABILITY. Carnegie Mellon University.
    http://www.stat.cmu.edu/~genovese/class/iprob-S06/
13. Janko GRAVNER. 2011. Lecture Notes for Introductory Probability University of California, Davis. Markov chains start in Chapter 15.
    http://www.math.ucdavis.edu/~gravner/MAT135A/resources/lecturenotes.pdf
14. Robert M. GRAY. Probability, Random Processes, and Ergodic Properties. Stanford University. 2008.
    http://www-ee.stanford.edu/~gray/arp.pdf
15. Charles GRINSTEAD and J. Laurie SNELL. Introduction to Probability.
    Chapter 10 Branching processes. Chapter 11 Markov chains.
    http://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/book.pdf
16. Bruce HAJEK. Notes for ECE 534.
    An Exploration of Random Processes for Engineers
    July, 2006.
    http://www.ifp.uiuc.edu/~hajek/Papers/randomprocesses.html
17. Kiyoshi IGUSA. 2008. Notes for course Math 56a. Brandeis University. Markov chains.
    http://people.brandeis.edu/~igusa/Math56aS08/Math56aS2008.htm#Notes
    Also
    http://people.brandeis.edu/~igusa/Math56F06/Math56a_lectures.pdf
18. JACOBSEN, KEIDING, MARTINUSSEN, NIELSEN, MADSEN, NIELSEN, BRIX. 2007. Problems in Markov chains. 34 pp.
    http://www.math.ku.dk/~susanne/kursusstokproc/ProblemsMarkovChains.pdf
19. Frank P. KELLEY. 1979. Reversibility and Stochastic Networks. A classic text. 235 pp.
    http://www.statslab.cam.ac.uk/~frank/BOOKS/kelly_book.html
20. Takis KONSTANTOPOULOS. 2009. Markov Chains and Random Walks. 128 pp.
    https://mcube.nctu.edu.tw/~cfung/docs/books/konstantopoulous2009_mcrw.pdf
21. Ger KOOLE. Lecture notes "Optimization of business processes." 2010. 246 pp.
    http://www.math.vu.nl/~koole/obp/obp.pdf
22. Peter KRAMER. 2010. Introduction to Stochastic Processes. MATH 6790-1, Rennsaeller Polytechnical Institute.
    http://www.rpi.edu/~kramep/Stoch/stoch2010.html
23. Steve LALLEY. 2007. Course Notes for Stat 313: Stochastic Processes. MCMC. Continuous time MC, martingales, Wiener processes. U. of Chicago.
    http://galton.uchicago.edu/~lalley/Courses/313/
24. Robert LIPSTER. Department of Electrical Engineering-Systems , Tel Aviv University. Lecture notes on ``Stochastic Processes'' and on ``Stochastic Control''
    http://www.eng.tau.ac.il/~liptser/
25. John LUI. Computer System Performance Evaluation (CSC5420). 2009. Hong Kong.
    http://www.cse.cuhk.edu.hk/~cslui/csc5420_lecture.html
26. Russell LYONS. Indiana University. Stochastic Processes. 104 pp.
    https://rdlyons.pages.iu.edu/pdf/StochProc.pdf
27. Ravi MAZUMDAR. 2009. ECE604. Stochastic Processes. University of Waterloo.
    http://www.ece.uwaterloo.ca/~mazum/ECE604/Lecture_Notes/
28. Sean MEYN & Richard TWEEDIE, Markov Chains and Stochastic Stability, Springer, 1996
    Winner of the 1994 ORSA/TIMS Award for the best research publication in Applied Probability. 536 pages.
    http://probability.ca/MT/
29. Oana MOCIOALCA. 2009. MATH-40051 and MATH-50051. TOPICS IN PROBABILITY THEORY AND STOCHASTIC PROCESSES University of Kent.
    http://www.math.kent.edu/~oana/math50051/
30. Soren Nielsen. Continuous-time homogeneous Markov chains. 32 pp.
    http://www.math.ku.dk/~susanne/kursusstokproc/ContinuousTime.pdf
31. Lecture Notes on Stochastic Processes Frank Noe, Bettina Keller and Jan-Hendrik Prinz July 17, 2013
    http://www.mi.fu-berlin.de/wiki/pub/CompMolBio/MarkovKetten15/stochastic_processes_2011.pdf
32. James NORRIS. Part of the book "Markov Chains" by J. R. Norris, University of Cambridge
    http://www.statslab.cam.ac.uk/~james/Markov/
33. Pekka ORPONEN. 2003. T-79.250 Combinatorial Models and Stochastic Algorithms Spring 2003. Helsinki University of Technology.
    http://www.tcs.hut.fi/Studies/T-79.250/
34. Bruce REED. 2008. 308-760B Applied Stochastic Processes. McGill University.
    http://cgm.cs.mcgill.ca/~breed/MATH671/
35. Simon RUBINSTEIN-SALZEDO. 2005. Probability Theory and Stochastic Processes. Notes from PStat 213A with Professor Raya Feldman. University of Albany.
    http://www.albanyconsort.com/simon/ProbTheoryNotes.pdf
36. M. SCOTT. 2013. Applied Stochastic Processes in science and engineering. 309 pp. Brownian Motion. Random Processes. Markov Processes. Master Equation. Perturbation. Fokker-Planck. Stochastic Calculus. Random Differential Equations.
    http://www.math.uwaterloo.ca/~mscott/Little_Notes.pdf
37. Mehrdad SHAHSHAHANI. (Iran) Introduction to stochastic processes,
    http://math.ipm.ac.ir/shahshahani/
38. Volker SCHMIDT. 2006. Markov Chains and Monte-Carlo Simulation. Lecture Notes. University Ulm, Department of Stochastics.
    http://www.mathematik.uni-ulm.de/stochastik/lehre/ss06/markov/skript_engl/
39. Stochastic Processes, Web course, NPTEL Phase II (with Prof. N. Selvaraju)
    http://nptel.ac.in/courses/111103022/
40. Karl SIGMAN. Stochastic Modeling Course. Lecture Notes, Columbia University, New York, 2001.
    http://www.columbia.edu/~ks20/stochastic-I/stochastic-I.html
41. Janos Sztrik. Modeling and Analysis of Information Technology Systems. by Dr. János Sztrik. University of Debrecen, Faculty of Informatics. 2011. Nice online book. 115 pp.
    http://irh.inf.unideb.hu/user/jsztrik/education/16/IRMA_Main_Angol.pdf
42. Glen TAKAHARA. 2010. STAT 455/855 course notes. Queen's University. Kingston, Ontario.
    http://www.mast.queensu.ca/~stat455/lecturenotes/lecturenotes.shtml
43. Ramon VAN HANDEL. 2008. Hidden Markov Models Lecture Notes.
    http://www.princeton.edu/~rvan/orf557/hmm080728.pdf
44. S.R.Srinivasa VARADHAN. 2011. Stochastic Processes Notes. Courant Institute of Mathematical Sciences New York University
    http://www.math.nyu.edu/faculty/varadhan/advancedtopics.spring11.html
45. J. VRBIK. Lecture Notes and Old Exams. (for a variety of courses including Stochastic Processes). Brock University.
    http://spartan.ac.brocku.ca/~jvrbik/
46. Ward WHITT. 2009. Lecture Notes for IEOR 4106. Columbia University.
    http://www.columbia.edu/~ww2040/4615S15/LectureNotes.html
47. Matthias WINKEL. 2007. "Applied Probability" Notes. 98 pp.
    http://www.stats.ox.ac.uk/~winkel/bs3a07.pdf
48. Serdar YUKSEL. Control of Stochastic Systems. Course Notes. Winter 2011. Queen’s Universit. Kingston, Ontario.
    https://mast.queensu.ca/~math472/LectureNotesOnStochasticControl.pdf
49. Gordan ZITKOVIC. UNIVERSITY OF TEXAS AT AUSTIN M362M: Introduction to Stochastic Processes
    https://web.ma.utexas.edu/users/gordanz/lecture_notes_page.html

1. Alan BAIN. "Stochastic Calculus." 95 pp.
   http://www.chiark.greenend.org.uk/~alanb/stoc-calc.pdf
2. Klaus BICHTELER. Stochastic integrations and Stochastic Differential Equations. University of Texas.
   http://www.ma.utexas.edu/users/kbi/SDE/C_1.html
3. Loren COBB. 1998. Stochastic Differential Equations for the Social Sciences. 26 pp.
   http://www.aetheling.com/docs/SDE.pdf
4. Jesper CARLSSO, Kyoung-Sook MOON, Anders SZEPESSY, Raul TEMPONE, Georgios ZOURARIS. 2010. Stochastic and Partial Differential Equations with Adapted Numerics, 202 pp.
   http://www.math.kth.se/~szepessy/sdepde.pdf
5. Lawrence C. EVANS. AN INTRODUCTION TO STOCHASTIC DIFFERENTIAL EQUATIONS, VERSION 1.2, UC Berkeley. 130 pp.
   http://math.berkeley.edu/~evans/SDE.course.pdf
6. David Gamarnik, Premal Shah. 15.070 Advanced Stochastic Processes. MIT. 2005.
   http://ocw.mit.edu/courses/sloan-school-of-management/15-070-advanced-stochastic-processes-fall-2005/index.htm
7. Jonathon GOODMAN. 2004. Stochastic Calculus Lecture Notes. New York University.
   http://math.nyu.edu/faculty/goodman/teaching/StochCalc2004/index.html
8. Martin HAUGH. 2010. Introduction to Stochastic Calculus.
   http://www.columbia.edu/~mh2078/stochastic_calculus.pdf
9. Davar Khoshnevisan. Stochastic Calculus. Math 7880-1 Spring 2008. University of Utah
   http://www.math.utah.edu/~davar/ps-pdf-files/SDE.pdf
10. Thomas KURTZ. Math 735 Stochastic Differential Equations notes. Lectures on Stochastic Analysis. U of Wisconsin
    http://www.math.wisc.edu/~kurtz/m735.htm
11. Haijun Li. MATH 490/583 (An Introduction to Stochastic Calculus), Fall 2010, Wayne State Univ.
    http://www.math.wsu.edu/math/faculty/lih/ms583.htm
12. Adam MONOHAN. 1998. Stochastic Differential Equations: A SAD Primer, 9 pp.
    http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.134.3249&rep=rep1&type=pdf
13. David NUALART. "Stochastic Calculus" 89 pp.
    http://www.math.ku.edu/~nualart/StochasticCalculus.pdf
14. P.J.C. SPREIJ. 2011. Stochastic integration. 97 pp.
    http://staff.science.uva.nl/~spreij/onderwijs/master/si.pdf
15. Ramon VAN HANDEL. 2007. Stochastic Calculus, Filtering, and Stochastic Control. Lecture Notes.
    http://www.princeton.edu/~rvan/acm217/ACM217.pdf
16. S.R.Srinivasa VARADHAN. 2000. Stochastic Processes Notes. Courant Institute of Mathematical Sciences New York University
    http://www.math.nyu.edu/faculty/varadhan/processes.html

1. Robert M. ANDERSON. Lecture Notes on Measure and Probability Theory. 2002. U. California (Berkeley)
   http://elsa.berkeley.edu/users/anderson/Econ204/MeasureTheoryLectureNotesTimeless.pdf
2. Rodrigo BANUELOS. 2003. Lecture Notes: Measure Theory and Probability. 198 pp.
   http://www.math.purdue.edu/~banuelos/probability.pdf
3. Richard BASS (U of Connecticut) Probability Notes. 2001
   http://www.math.uconn.edu/~bass/prob.pdf
   For other lecture notes on measure theory, stochastic calculus, financial mathematics, undergraduate probability, by Richard Bass, go to
   http://www.math.uconn.edu/~bass/lecture.html
4. Richard BASS. 2008. Stochastic Processes Notes. U of Connecticut. Measure theoretic.
   http://www.math.uconn.edu/~bass/stochproc.pdf
   For other lecture notes on probability, measure theory, stochastic calculus, financial mathematics, undergraduate probability, by Richard Bass, go to
   http://www.math.uconn.edu/~bass/lecture.html
5. Dimitri P. BERTSEKAS and Steven E. SHREVE. 1978. Stochastic Optimal Control: The Discrete-Time Case, by Academic Press 1978. Republished by Athena Scientific, 1996. Discusses finite and infinite horizon models. Measure theoretic.
   http://web.mit.edu/dimitrib/www/soc.html
6. Herman J. BIERENS. 2003. Probability and Measure. Pennsylvania State University
   http://econ.la.psu.edu/~hbierens/PROBABIL.PDF
7. Michael Bishop. 2010. Measure and Probability.
   http://math.arizona.edu/~jgemmer/bishopprobability1.pdf
   http://math.arizona.edu/~jgemmer/bishopprobability1.pdf
   http://math.arizona.edu/~jgemmer/bishopprobability1.pdf
8. Stefano BONACCORSI and Enrico PRIOLA. 2006. Brownian Motion and Stochastic Differential Equations
   http://cantor.mathematik.uni-ulm.de/m5/einemann/teaching/isem_0607/
9. Amir DENBO. 2008. Stochastic Processes (MATH136, Stanford). , Measure theoretic. 131 pp.
   http://www-stat.stanford.edu/~amir/math-136/
10. Bruce K. DRIVER. 2007. Math 280 (Probability Theory) Lecture Notes. 233 pp.
    http://www.math.ucsd.edu/~bdriver/280_06-07/Lecture_Notes/N16_2p.pdf
11. Alexander GRIGORYAN. 2008. Measure theory and probability. Lecture notes. 122 pp.
    http://www.math.uni-bielefeld.de/~grigor/mwlect.pdf
12. HOU Zhenting, GUO Qingfeng. 1988. Homogeneous Denumerable Markov Processes, Springer-Verlag, Germany, Science Press, Beijing, 282 pages. (measure theoretic)
    http://prob.csu.edu.cn/hou/books/Denu1988.pdf
13. Davar Khoshnevisan. Math 6040. The University of Utah. Mathematical Probability
    http://www.math.utah.edu/~davar/probtheory.pdf
14. Oliver KNILL. 2006. Probability. Caltech. (Measure theoretic) 377 pp.
    http://www.math.harvard.edu/~knill/teaching/math144_1994/probability.pdf
15. Vladimir Semenovich Koroliuk, Nikolaos Limnios. Chapter 1 of Stochastic systems in merging phase space.
    http://www.worldscibooks.com/etextbook/5979/5979_chap1.pdf
16. Michael KOZDRON. Probability. (based on J. Rosenthal's A First Look at Rigorous Probability Theory)
    http://stat.math.uregina.ca/~kozdron/Teaching/Regina/851Winter08/Handouts/851notes_L1_to_L36.pdf
17. Thomas G. KURTZ. 2000. LMS/EPSRC Short Course on Stochastic Analysis. Oxford University.
    http://www.math.wisc.edu/~kurtz/oxford/ox_outline.html
18. Greg LAWLER. Probability Notes. U. of Chicago.
    http://www.math.uchicago.edu/~lawler/probnotes.pdf
19. Gregory MIERMONT. Advanced Probability. Part III of the Mathematical Tripos. 2006 92 pp.
    http://www.math.u-psud.fr/~miermont/AdPr2006.pdf
20. Peter MORTERS. 1999-2000. Lecture Notes in Probability, Universitat Kaiserslautern.
    http://people.bath.ac.uk/maspm/prob.ps
21. Peter MORTERS. 2000. Lecture Notes in Stochastic Processes, Universitat Kaiserslautern. Martingales. Stochastic Integrals. Stochastic Calculus. Brownian Motion.
    http://people.bath.ac.uk/maspm/stoa.ps
22. Efe A. OK (New York University). Probability Theory with Economic Applications.
    http://homepages.nyu.edu/~eo1/books.html
23. Yuval PERES. 2002. Statistics 205B : Probability Theory II notes. Berkeley.
    http://stat-www.berkeley.edu/~peres/teach/
24. Jorn SASS. 2010. Probability Theory I.
    http://www.mathematik.uni-kl.de/~sass/eng/teaching/VorlWT/WT1V.pdf
25. M. SCHWEIZER. Stochastic Processes and Stochastic Analysis. 2007. 75 pp.
    http://www.mitschriften.ethz.ch/main.php?page=3&scrid=1&pid=17&oid=4&eid=2
26. Timo SEPPALAINEN. Basics of Stochastic Analysis. 2010
    Measure theory, Filtrations and stopping times, Markov property, Brownian motion, Poisson processes, Martingales, Stochastic Integral, Itoˆ'’s forlmua, Stochastic Differential Equations
    http://www.math.wisc.edu/~seppalai/courses/735/notes.pdf
27. Cosma SHALIZI with Aryeh Kontorovich. 2010. 36-754, Advanced Probability II
    Almost None of the Theory of Stochastic Processes. 320 pp.
    http://staff.science.uva.nl/~spreij/onderwijs/master/oldmtp.pdf
28. Terence TAO. 254A, Notes 0: A review of probability theory. 2010.
    http://terrytao.wordpress.com/2010/01/01/254a-notes-0-a-review-of-probability-theory/
29. Noel VAILLANT. Probability Tutorials. More measure theory than probability, but all with probability in mind. Very interesting site.
    http://www.probability.net/
30. I.F. WILDE. Measure, integration & probability. King's College, London.
    http://www.mth.kcl.ac.uk/~iwilde/notes/mip/
31. Virtual Laboratories in Probability and Statistics.
    http://www.math.uah.edu/stat/
32. Youtube Video.
    Measure Theory for Probability Primer: by mathematicalmonk.
    
33. Youtube video. Chris Evans. Why use measure theory for probability? 3 parts
    Part 1
    Part 2
    Part 3

1. Michael P. MCLAUGHLIN. Compendium of Distributions.
   http://www.geocities.com/~mikemclaughlin/math_stat/Dists/Compendium.pdf
2. Continuous Distributions.
   http://www.xycoon.com/contdistroverview.htm
3. Engineering Statistics Handbook
   NIST/SEMATECH e-Handbook of Statistical Methods
   Contains distributions in Chapter 1 (Explore)
   http://www.itl.nist.gov/div898/handbook/index.htm

1. Amir DEMBO. Lecture Notes on Stochastic Processes including a chapter on Brownian Motion
   http://www-stat.stanford.edu/~adembo/math-136/nnotes.pdf
2. Lawrence EVANS. Lecture notes on Stochastic Differential Equations
   http://math.berkeley.edu/~evans/SDE.course.pdf
3. Steve LALLEY. Lecture Notes on mathematical finance
   http://www.stat.uchicago.edu/~lalley/Courses/390/
4. Feng YU. Lecture notes on Brownian Motion
   http://www.maths.bris.ac.uk/~maxfy/sp.html
5. Wikipedia article on Brownian Motion.
   http://en.wikipedia.org/wiki/Brownian_motion
6. A Java applet simulating Brownian Motion.
   http://xanadu.math.utah.edu/java/brownianmotion/1/

1. Scot ADAMS and Fernando REITICH. Notes on Financial Mathematics
   http://www.math.umn.edu/finmath/lectures/
2. Stefan ANKIRCHNER. Option Pricing and Financial Mathemtics.
   http://www.iam.uni-bonn.de/people/ankirchner/teaching.html
3. Marco AVELLANEDA. 1996. Topics in Probability: The mathematics of financial risk-management. Courant Institute of Mathematical Sciences
   http://www.math.nyu.edu/faculty/avellane/risk.html
4. Richard F. BASS. 2003. The Basics of Financial Mathematics. 106 pp.
   http://www.math.uconn.edu/~bass/finlmath.pdf
5. Michael KOZDRAN. Stochastic Calculus with Applications to Finance 2009
   http://stat.math.uregina.ca/~kozdron/Teaching/Regina/441Winter09/Notes/441_L1_L36.pdf
6. Holger KRAFT. 2005. Lecture Notes. “FINANCIAL MATHEMATICS I: Stochastic Calculus, Option Pricing, Portfolio Optimization. ” University of Kaiserslautern.
   http://elsa.ub.uni-kl.de/ressource56/
7. Harald LANG. Lectures on Financial Mathematics
   http://www.math.kth.se/~lang/finansmatte/fin_note.pdf
8. Vasily NEKRASOV. Yet another, yet very reader-friendly, introduction to measure theory (for financial mathematics).
   http//www.yetanotherquant.com
9. M. NEWBY and P.P. MARTIN Mathematics for Finance: Mathematical Processes for Finance, 2005. by
   http://staff.city.ac.uk/~ra359/X3MathFinance/
10. Karl SIGMAN. Notes on Financial Engineering.
    http://www.columbia.edu/~ks20/FE-Notes/FE-Notes-Sigman.html
11. E. TICK. Financial Engineering Course Notes.
    http://lsc.fie.umich.mx/~juan/Materias/Posgrado/Finance/fin/fin.html
12. Fabio TROJANI. Introduction to Probability Theory and Stochastic Processes for Finance. Lecture Notes. 98 pp.
    http://www.people.lu.usi.ch/trojanif/Master_USI/Probability_0405/LectureNotes/Lecturenotes.pdf
13. A.W. VAN DER VAART (Vrije U). Martingales, Diffusions, and Financial Mathematics
    http://www.math.vu.nl/sto/onderwijs/fw/stochint.pdf

1. Persi DIACONIS. The Markov Chain Monte Carlo Revolution
   http://www-stat.stanford.edu/~cgates/PERSI/papers/MCMCRev.pdf
2. Charles J. GEYER. 2005. Markov Chain Monte Carlo Lecture Notes. 162 pp.
   http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf
3. Antonietta MIRA. Introduction to Monte Carlo and MCMC Methods.
   http://venda.uku.fi/research/FIPS/BMIP/pdffiles/mcmc-uk.pdf
4. R.M. NEAL. 1993. Probabilistic Inference Using Markov Chain Monte Carlo Methods, Technical Report CRG-TR-93-1. 144 pp.
   http://www.cs.toronto.edu/~radford/ftp/review.pdf
5. ZHU, DELLEART and TU. Markov Chain Monte Carlo for Computer Vision --- A tutorial.
   http://civs.ucla.edu/old/MCMC/MCMC_tutorial.htm

1. Prakash BALACHANDRAN. 2008. Fundamental Inequalities, Convergence and the Optional Stopping Theorem for Continuous-Time Martingales. 13 pp.
   http://www.duke.edu/~pb25/Math/continuous-time%20martingales.pdf
2. Michael KOZDRON. Martingales. 5 pp.
   http://stat.math.uregina.ca/~kozdron/Teaching/Regina/862Winter06/Handouts/mart.pdf
3. Steve LALLEY. Martingale Lectures. 15 pp.
   http://www.stat.uchicago.edu/~lalley/Courses/390/Lecture3.pdf
4. Kevin ROSS. Optional Stopping.
   http://www.swarthmore.edu/NatSci/kross1/Lec14_1027.pdf
   http://www.swarthmore.edu/NatSci/kross1/Lec15_1029.pdf
   http://www.swarthmore.edu/NatSci/kross1/Lec16_1031.pdf
   http://www.swarthmore.edu/NatSci/kross1/Lec17_1103.pdf
5. Karl SIGMAN. Introduction to Martingales in discrete time. 8 pp.
   http://www.columbia.edu/~ks20/stochastic-I/stochastic-I-MG-Intro.pdf
6. Alistair SINCLAIR. Martingales and the Optional Stopping Theorem. 6 pp.
   http://www.cs.berkeley.edu/~sinclair/cs271/n21.pdf
7. A.W. VAN DER VAART (Vrije U). Martingales, Diffusions, and Financial Mathematics
   http://www.math.vu.nl/sto/onderwijs/fw/stochint.pdf
8. John B. WALSH. Notes on Elementary Martingale Theory. 44 pp.
   http://www.math.ubc.ca/~walsh/marts.pdf
9. Hans Gerber lectures on martingale theory. Video.
   http://www.personal.psu.edu/afs1/gerber_m/
10. Stochastic Processes in Continuous Time. Notes. U of Arizona. Joseph C. Watkins. 2007
    http://math.arizona.edu/~jwatkins/notesc.pdf