Maximum likelihood estimation: example of mle on boundary of parameter space
Finding the mle when the solution is on the boundary of the parameter space. Example from 2009 ST102 past paper question 6c.
Phil Chan
Random Variables (FRM Part 1 2020 – Book 2 – Chapter 2)
AnalystPrep's FRM Part 1 Video Series For FRM Video Lessons, Study Notes, Practice Questions, and Mock Exams Register an Account at ...
AnalystPrep
Stochastic Calculus and Processes: Introduction (Markov, Gaussian, Stationary, Wiener, and Poisson)
Introduces Stochastic Calculus and Stochastic Processes. Covers both mathematical properties and visual illustration of important processes. Explain ...
quantpie
Stationary Time Series (FRM Part 1 2020 – Book 2 – Chapter 10)
AnalystPrep's FRM Part 1 Video Series For FRM Part 1 Study Notes, Practice Questions, and Mock Exams Register an Account at https://analystprep.com/frm/ ...
AnalystPrep
4. Parametric Inference (cont.) and Maximum Likelihood Estimation
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet In this lecture, Prof. Rigollet ...
MIT OpenCourseWare
Common Univariate Random Variables (FRM Part 1 2020 – Book 2 – Chapter 3)
AnalystPrep's FRM Part 1 Video Series For FRM Part 1 Study Notes, Practice Questions, and Mock Exams Register an Account at https://analystprep.com/frm/ ...
AnalystPrep
Hypothesis Testing and Confidence Intervals (FRM Part 1 – Book 2 – Chapter 5)
AnalystPrep's FRM Part 1 Video Series For FRM Part 1 Study Notes, Practice Questions, and Mock Exams Register an Account at https://analystprep.com/frm/ ...
AnalystPrep
Measuring Sample Quality with Stein's Method
To improve the efficiency of Monte Carlo estimation, practitioners are turning to biased Markov chain Monte Carlo procedures that trade-off asymptotic exactness ...
Microsoft Research
Gérard Ben Arous (1.1) Gaussian Random Matrices and stationary random functions, part 1.1
Gérard Ben Arous, Courant Institute of Mathematical Sciences, New York University. Presented at the 27th Annual PCMI Summer Session, Random Matrices, ...
PCMI-a Program of the Institute for Advanced Study
Theory and Algorithms for Forecasting Non-Stationary Time Series (NIPS 2016 tutorial)
Vitaly Kuznetsov, Mehryar Mohri Time series appear in a variety of key real-world applications such as signal processing, including audio and video processing; ...
Steven Van Vaerenbergh
Hypothesis Testing (FRM Part 1 2020 – Book 2 – Chapter 6)
AnalystPrep's FRM Part 1 Video Series For FRM Part 1 Study Notes, Practice Questions, and Mock Exams Register an Account at https://analystprep.com/frm/ ...
AnalystPrep
Ornstein Uhlenbeck (OU) Process: solution, mean, variance, covariance, calibration, and simulation
Step by step derivation of the Ornstein-Uhlenbeck Process' solution, mean, variance, covariance, probability density, calibration /parameter estimation, and ...
quantpie
Langevin and Fokker Planck equations
The Langevin equation is explained and analyzed. The time evolution of the velocity distribution is found in the form of the Fokker-Planck equation.
Jos Thijssen
Simulation and Bootstrapping (FRM Part 1 2020 – Book 2 – Chapter 13)
AnalystPrep's FRM Part 1 Video Series For FRM Video Lessons, Study Notes, Practice Questions, and Mock Exams Register an Account at ...
AnalystPrep
Lesson 11 Continuous Random Variables
The probability density function and the cumulative distribution function for a continuous random variable are explained in this lesson. For more lessons check ...
A Statistical Path
Intuitive proofs of Ergodic Theorems
Ergodic Theorems are widely used in dynamical systems and Probability Theory. In this expository lecture, I will present simple proofs of the Birkhoff Pointwise ...
Microsoft Research
Spectral and Wavelet Coherence for Point Processes: A Tool for Cyber
Computer networks can be represented by (marked) point processes communicating information between nodes. Developing methodologies for finding and ...
Microsoft Research
Sub-Gaussian Mean Estimation in Polynomial Time
Samuel Hopkins (UC Berkeley) https://simons.berkeley.edu/talks/sub-gaussian-mean-estimation-polynomial-time Robust and High-Dimensional Statistics.
Simons Institute
Stochastic Modelling of Coronavirus spread
Part 2 of the series explains the stochastic modelling framework for the modelling of the spread of infectious diseases such as Coronavirus. It explains the ...
quantpie
Poisson Distribution
In this lesson we derive the expectation, variance, and moment generating function of Poisson random variable. The derivation of the Poisson PMF is provided at ...
A Statistical Path
Mod-01 Lec-36 The Wiener process (standard Brownian motion)
Nonequilibrium Statistical Mechanics by Prof. V. Balakrishnan, Department of Physics, IIT Madras.For more details on NPTEL visit http //nptel.ac.in.
nptelhrd
Probabilistic Programming in the Real World - Zach Anglin
PyData DC 2018 Probabilistic programming frameworks get a lot of press, but relatively little attention is paid to the indicators that a problem is a good fit for a ...
PyData
Lec 17 | MIT 6.450 Principles of Digital Communications I, Fall 2006
Lecture 17: Detection for random vectors and processes View the complete course at: http://ocw.mit.edu/6-450F06 License: Creative Commons BY-NC-SA More ...
MIT OpenCourseWare
Self-Organizing Cellular Automata
Cellular automata display an extraordinary range of behavior, ranging from very simple to apparently chaotic, with many cases in between. Perhaps the most ...
Microsoft Research
12 exchangeability and iid
An explanation of the relationship between exchangeability and the assumption that sequences are independent, and identically distributed (iid). If you are ...
Ox educ
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 7 - Imitation Learning
Professor Emma Brunskill, Stanford University http://onlinehub.stanford.edu/ Professor Emma Brunskill Assistant Professor, Computer Science Stanford AI for ...
stanfordonline
R Tutorial : Poisson regression
Want to learn more? Take the full course at https://learn.datacamp.com/courses/generalized-linear-models-in-r at your own pace. More than a video, you'll learn ...
DataCamp
Environmental / Spatial Statistics: Climate Change
(1662) Statistics for Heatwaves and Extreme Waves in a Changing Climate Jonathan Tawn - Lancaster University, United Kingdom (1616) Bayesian Additive ...
RoyalStatSoc
Statistical Modeling - Learn Data Science with XLSTAT
Statistical modeling allows to investigate how variables change according to other variables, and to make predictions. Download presentation material: ...
XLSTAT
Sensitivity with Stella
An integral part of working with models is seeing how behavior changes as you vary inputs and assumptions. You can do this by editing equations or adjusting ...
isee systems
5. Maximum Likelihood Estimation (cont.)
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet In this lecture, Prof. Rigollet ...
MIT OpenCourseWare
Charaterizing Cycles (FRM Part 1 – Book 2 – Chapter 12)
AnalystPrep's FRM Part 1 Video Series For FRM Part 1 Study Notes, Practice Questions, and Mock Exams Register an Account at https://analystprep.com/frm/ ...
AnalystPrep
JuliaCon 2019 | Turing: Probabalistic Programming in Julia | Cameron Pfiffer
Turing is a probabilistic programming language written in Julia. This talk will introduce Turing and its tooling ecosystem, as well as go over some introductory ...
The Julia Programming Language
Bayesian Data Analysis of Nonparametric Models in Clojure - Michael Lindon
A research team of statisticians and neuroscientists at Duke University are using Clojure to analyze how neural codes in the brain encode information from two ...
ClojureTV
Contextual Bandit: from Theory to Applications. - Vernade - Workshop 3 - CEB T1 2019
Claire Vernade (Google Deepmind) / 05.04.2019 Contextual Bandit: from Theory to Applications. Trading exploration versus exploration is a key problem in ...
Institut Henri Poincaré
Mod-01 Lec-17 Numerical Examples in Diffusion
Principles of Physical Metallurgy by Prof. R.N. Ghosh,Department of Metallurgy and Material Science,IIT Kharagpur.For more details on NPTEL visit ...
nptelhrd
Machine learning - Gaussian processes
Regression with Gaussian processes Slides available at: http://www.cs.ubc.ca/~nando/540-2013/lectures.html Course taught in 2013 at UBC by Nando de ...
Nando de Freitas
Financial time series (QRM Chapter 4)
29th International Summer School of the Swiss Association of Actuaries (2016-08-15, Lausanne). For the corresponding course material, see ...
QRM Tutorial
The Importance of Better Models in Stochastic Optimization...
John Duchi (Stanford University) https://simons.berkeley.edu/talks/tbd-28 Robust and High-Dimensional Statistics.
Simons Institute
Time Series ARIMA Models
Time Series ARIMA Models https://sites.google.com/site/econometricsacademy/econometrics-models/time-series-arima-models.
econometricsacademy
Lec 16 | MIT 6.450 Principles of Digital Communications I, Fall 2006
Lecture 16: Review; introduction to detection View the complete course at: http://ocw.mit.edu/6-450F06 License: Creative Commons BY-NC-SA More information ...
MIT OpenCourseWare
Yan Fyodorov (3.1) Counting equilibria in complex systems via random matrices, part 3.1
Yan Fyodorov, King's College London, London. Lecture notes available at ...
PCMI-a Program of the Institute for Advanced Study
