4.7 Brownian Bridge(part 2) 報告人:李振綱. Outline 4.7.4 Multidimensional Distribution of the Brownian Bridge 4.7.4 Multidimensional Distribution of the Brownian.

Slides:



Advertisements
Similar presentations
Random Processes Introduction (2)
Advertisements

Some additional Topics. Distributions of functions of Random Variables Gamma distribution,  2 distribution, Exponential distribution.
Let X 1, X 2,..., X n be a set of independent random variables having a common distribution, and let E[ X i ] = . then, with probability 1 Strong law.
Tangent lines Recall: tangent line is the limit of secant line The tangent line to the curve y=f(x) at the point P(a,f(a)) is the line through P with slope.
Chapter 3 Brownian Motion 報告者:何俊儒.
SOLVED EXAMPLES.
Dividend-Paying Stocks 報告人:李振綱 Continuously Paying Dividend Continuously Paying Dividend with Constant Coefficients Lump Payments of.
Chain Rules for Entropy
Lecture note 6 Continuous Random Variables and Probability distribution.
3.5 Markov Property 劉彥君. Introduction In this section, we show that Brownian motion is a Markov process and discuss its transition density.
4.7 Brownian Bridge 報告者 : 劉彥君 Gaussian Process Definition 4.7.1: A Gaussian process X(t), t ≥ 0, is a stochastic process that has the property.
Probability Densities
From PET to SPLIT Yuri Kifer. PET: Polynomial Ergodic Theorem (Bergelson) preserving and weakly mixing is bounded measurable functions polynomials,integer.
AGC DSP AGC DSP Professor A G Constantinides© Estimation Theory We seek to determine from a set of data, a set of parameters such that their values would.
Chapter 6 Continuous Random Variables and Probability Distributions
3.3 Brownian Motion 報告者:陳政岳.
3.7 Reflection Principle 報告人 : 李振綱 Reflection Equality First Passage Time Distribution Distribution of Brownian Motion and Its Maximum.
2.3 General Conditional Expectations 報告人:李振綱. Review Def (P.51) Let be a nonempty set. Let T be a fixed positive number, and assume that for each.
Continuous Random Variables and Probability Distributions
Chapter 5 Continuous Random Variables and Probability Distributions
4.4 Itô-Doeblin Formula 報告人:劉彥君.
4.4 Ito-Doeblin Formula(part2) 報告人:李振綱. The integral with respect to an Ito process Ito-Doeblin formula for an Ito process Example  Generalized geometric.
Random Variable and Probability Distribution
Lecture II-2: Probability Review
1 10. Joint Moments and Joint Characteristic Functions Following section 6, in this section we shall introduce various parameters to compactly represent.
Separate multivariate observations
Maximum Likelihood Estimation
Definition of Covariance The covariance of X & Y, denoted Cov(X,Y), is the number where  X = E(X) and  Y = E(Y). Computational Formula:
Random variables Petter Mostad Repetition Sample space, set theory, events, probability Conditional probability, Bayes theorem, independence,
SIMPLIFY EXPRESSIONS WITH INTEGER EXPONENTS PRACTICE ALL OF THE PROPERTIES OF EXPONENTS.
Andy Guo 1 Handout Ch5(2) 實習. Andy Guo 2 Normal Distribution There are three reasons why normal distribution is important –Mathematical properties of.
Combined Uncertainty P M V Subbarao Professor Mechanical Engineering Department A Model for Propagation of Uncertainty ….
ECE 8443 – Pattern Recognition LECTURE 03: GAUSSIAN CLASSIFIERS Objectives: Normal Distributions Whitening Transformations Linear Discriminants Resources.
CHAPTER 4 Multiple Random Variable
PROBABILITY AND STATISTICS FOR ENGINEERING Hossein Sameti Department of Computer Engineering Sharif University of Technology Two Functions of Two Random.
1 7. Two Random Variables In many experiments, the observations are expressible not as a single quantity, but as a family of quantities. For example to.
5.4 Fundamental Theorems of Asset Pricing 報告者:何俊儒.
Modular 11 Ch 7.1 to 7.2 Part I. Ch 7.1 Uniform and Normal Distribution Recall: Discrete random variable probability distribution For a continued random.
Use of moment generating functions 1.Using the moment generating functions of X, Y, Z, …determine the moment generating function of W = h(X, Y, Z, …).
Chapter 5.6 From DeGroot & Schervish. Uniform Distribution.
Multiple Random Variables Two Discrete Random Variables –Joint pmf –Marginal pmf Two Continuous Random Variables –Joint Distribution (PDF) –Joint Density.
Physics Fluctuomatics/Applied Stochastic Process (Tohoku University) 1 Physical Fluctuomatics Applied Stochastic Process 3rd Random variable, probability.
PROBABILITY AND STATISTICS FOR ENGINEERING Hossein Sameti Department of Computer Engineering Sharif University of Technology Two Random Variables.
Operations on Multiple Random Variables
Physics Fluctuomatics (Tohoku University) 1 Physical Fluctuomatics 3rd Random variable, probability distribution and probability density function Kazuyuki.
Random Variables Ch. 6. Flip a fair coin 4 times. List all the possible outcomes. Let X be the number of heads. A probability model describes the possible.
Properties of Exponents – Part 1 Learn multiplication properties of exponents.
Continuous Random Variables and Probability Distributions
1 EE571 PART 3 Random Processes Huseyin Bilgekul Eeng571 Probability and astochastic Processes Department of Electrical and Electronic Engineering Eastern.
Distributions of Functions of Random Variables November 18, 2015
Joint Moments and Joint Characteristic Functions.
STA347 - week 91 Random Vectors and Matrices A random vector is a vector whose elements are random variables. The collective behavior of a p x 1 random.
S TOCHASTIC M ODELS L ECTURE 4 B ROWNIAN M OTIONS Nan Chen MSc Program in Financial Engineering The Chinese University of Hong Kong (Shenzhen) Nov 11,
Geology 6600/7600 Signal Analysis 04 Sep 2014 © A.R. Lowry 2015 Last time: Signal Analysis is a set of tools used to extract information from sequences.
6 vector RVs. 6-1: probability distribution A radio transmitter sends a signal to a receiver using three paths. Let X1, X2, and X3 be the signals that.
Multiplying with exponents
Cumulative distribution functions and expected values
PRODUCT MOMENTS OF BIVARIATE RANDOM VARIABLES
Quantum Two.
Example Suppose X ~ Uniform(2, 4). Let . Find .
EMIS 7300 SYSTEMS ANALYSIS METHODS FALL 2005
5.4 General Linear Least-Squares
Random WALK, BROWNIAN MOTION and SDEs
Evaluate when A.) 18 B.) 243 C.) 729 C.) 729 D.) 27 L F.
Chapter 3 Brownian Motion 洪敏誠 2009/07/31 /23.
PARTIAL DIFFERENTIATION 2
9. Two Functions of Two Random Variables
Introduction to Probability: Solutions for Quizzes 4 and 5
5.3 Martingale Representation Theorem
Presentation transcript:

4.7 Brownian Bridge(part 2) 報告人:李振綱

Outline Multidimensional Distribution of the Brownian Bridge Multidimensional Distribution of the Brownian Bridge Brownian Bridge as a Conditioned Brownian Motion Brownian Bridge as a Conditioned Brownian Motion

4.7.4 Multidimensional Distribution of the Brownian Bridge We fix and and let denote the Brownian bridge from a to b on. We also fix. We compute the joint density of. We recall that the Brownian bridge from a to b has the mean function (P.176) and covariance function (P.176) When, we may write this as To simplify notation, we set so that.

We define random variable Because are jointly normal, so that are jointly normal. We compute, and. Brownian Bridge as Gaussian Process (4.7.2)

X,Y are independent Cov(X,Y)=0 If X, Y are normal distribution 課本符號有錯

So we conclude that the normal random variable are independent, and we can write down their joint density, which is we make the change of variables mean Variance 接下來把 z 的部 份做變數變換

Where, to find joint density for. We work first on the sum in the exponent to see the effect of this change of variables. We have Now

So this last expression is equal to To change s density, we also need to account for the Jacobian of the change of variables. In this case, we have and all other partial derivatives are zero. This leads to the Jacobian matrix

Whose determinant is. Multiplying by this determinant and using the change of variables worked out above, we obtain the density for, Ch3-5 P.108

4.7.5 Brownian Bridge as a Conditioned Brownian Motion The joint density (4.7.6) for permits us to give one more interpretation for Brownian bridge from a to b on. It is a Brownian motion on this time interval, starting at and conditioned to arrive at b at time T (i.e., conditioned on ). Let be given. The joint density of is This is because is the density for the Brownian motion going from to in the time between and. Similarly, is the density for going from to between time and. The joint density for and is then the product ??

So, the density of conditioned on is thus the quotient and this is of (4.7.6). Finally, let us define to be the maximum value obtained by the Brownian bridge from a to b on. This random variable has the following distribution.

Corollary The density of is Proof : Because the Brownian bridge from 0 to on is a Brownian motion conditioned on, the maximum of on is the maximum of on conditioned on. Therefore, the density of was computed in Corollary and is The density of can be obtained by translating from the initial condition to and using (4.7.9). In particular, in (4.7.9) we replace by and replace by. This result in (4.7.8).

Thank you for your listening!!