11 - Markov Chains Jim VallandinghamSlide 2
Outline Irreducible Markov Chains Outline of Proof of Convergence to Stationary Distribution Convergence Example Reversible Markov Chain Monte Carlo Methods Hastings-Metropolis Algorithm Gibbs Sampling Simulated Annealing Absorbing Markov ChainsSlide 3
Stationary Distribution As methodologies Each column is the stationary appropriationSlide 4
Stationary Dist. IllustrationSlide 5
Stationary Dist. Case Long Term midpoints: 24% time invested in state E1 39% energy invested in state E2 21% time invested in state E3 17% time spent in state E4Slide 6
Stationary Distribution Any limited, aperiodic irreducible Markov bind will join to a stationary appropriation Regardless of beginning dissemination Outline of Proof requires straight variable based math Appendix B.19Slide 7
L.A. : Eigenvalues Let P be a s x s lattice. P has s eigenvalues Found as the s answers for Assume all eigenvalues of P are particularSlide 8
L.A. : left & right eigenvectors Corresponding to every eigenvalue Is a privilege eigenvector - And a left eigenvector - For which: Assume they are standardized:Slide 9
L.A. : Spectral Expansion Can express P as far as its eigenvectors and eigenvalues: Called a ghostly extension of PSlide 10
L.A. : Spectral Expansion If is an eigenvalue of P with relating left and right eigenvectors & Then is an eigenvalue of P n with same left and right eigenvectors &Slide 11
L.A. : Spectral Expansion Implies otherworldly development of P n can be composed as:Slide 12
Outline of Proof Going back to evidence… P is move grid for limited aperiodic irreducible Markov chain P has one eigenvalue, equivalent to 1 All different eigenvalues have outright esteem < 1Slide 13
Outline of Proof Choosing left and right eigenvectors of Requirements: Also fulfills : & = 1 Probability vector (total to 1) Normalization (meaning of left eigenvector as eigenvalue of 1)Slide 14
Outline of Proof Also: Can be demonstrated that there is a novel arrangement of this condition likewise fulfills so so that Same condition fulfilled by the stationary dispersionSlide 15
Outline of Proof P n gives the n-step move probabilities. Ghostly Expansion of P n is: So as n builds P n approaches Only one eigenvalue is = 1. Rest are < 1Slide 16
Convergence ExampleSlide 17
Convergence Example Has Eigenvalues of :Slide 18
Convergence Example Has Eigenvalues of : Less than 1Slide 19
Convergence Example Left & Right eigenvectors fulfillingSlide 20
Convergence Example Left & Right eigenvectors fulfilling Stationary disseminationSlide 21
Convergence Example Spectral development Stationary circulation 0Slide 22
Reversible Markov ChainsSlide 23
Reversible Markov Chains Typically pushing ahead in "time" in a Markov chain 1 2 3 … t What about going in reverse in this chain? t t-1 t-2 … 1Slide 24
Reversible Markov Chains Ancestor Back in time Forward in time Species A Species BSlide 25
Reversible Markov Chains Have a limited irreducible aperiodic Markov chain with stationary circulation During t moves, chain will travel through states: Reverse chain Define Then turn around chain will travel through states:Slide 26
Reversible Markov Chains Want to show structure deciding the switch chain grouping is likewise a Markov chain Typical component found from run of the mill component of P, utilizing:Slide 27
Reversible Markov Chains Shown by utilizing Bayes lead to transform restrictive likelihood Intuitively: what\'s to come is autonomous of the past , given the present The past is free without bounds , given the presentSlide 28
Reversible Markov Chains Stationary appropriation of invert chain is still Follows from Stationary dissemination propertySlide 29
Reversible Markov Chains Markov anchor is said to be reversible if This exclusive holds ifSlide 30
Monte Carlo MethodsSlide 31
Markov Chain Monte Carlo Class of calculations for inspecting from likelihood dispersions Involve building a Markov Chain Want to have stationary conveyance State of chain after expansive number of steps is utilized as an example of sought circulation We examine 2 calculations Gibbs Sampling Simulated AnnealingSlide 32
Basic Problem Find move lattice P to such an extent that Its stationary dissemination is the objective conveyance Know that Markov fasten will merge to stationary dissemination, paying little mind to starting appropriation How would we be able to discover such a P with its stationary circulation as the objective circulation?Slide 33
Basic Idea Construct move lattice Q "competitor creating network" Modify to have amend stationary appropriation Modification includes embeddings calculates So that Various approaches to picking a\'sSlide 34
Hastings-Metropolis Goal: build aperiodic irreducible Markov chain Having endorsed stationary dissemination Produces a related grouping of draws from the objective thickness that might be hard to test utilizing a traditional autonomy strategy.Slide 35
Hastings-Metropolis Process: Choose set of constants Such that And Define Accept state change Reject state change Chain doesn\'t change esteemSlide 36
Hastings-Metropolis Example = (.4 .6) Q =Slide 37
Hastings-Metropolis Example = (.4 .6) Q = P=Slide 38
Hastings-Metropolis Example = (.4 .6) P= P 2 = P 50 =Slide 39
Algorithmic Description Start with State E 1 , then emphasize Propose E\' from q(E t ,E\') Calculate proportion If a > 1, Accept E (t+1) = E\' Else Accept with likelihood of an If rejected, E (t+1) = E tSlide 40
Gibbs SamplingSlide 41
Gibbs Sampling Definitions Be the irregular vector Be the dispersion of Assume We characterize a Markov chain whose states are the conceivable estimations of YSlide 42
Gibbs Sampling Process Enumerate vectors in some request 1, 2,… ,s Pick vector j with jth state in chain p ij : 0 : if vectors i & j contrast by more than 1 part If they vary by at most 1 segment, y 1 *Slide 43
Gibbs Sampling Assume Joint dissemination p(X,Y) Looking to test k estimations of X Begin with estimation of y 0 Sample x i utilizing p(X | Y = y i-1 ) Once x i is discovered utilize it to discover y i p(Y | X = x i ) Repeat k timesSlide 44
Visual ExampleSlide 45
Gibbs Sampling Allows us to manage univariate restrictive appropriations Instead of complex joint circulations Chain has stationary conveyance ofSlide 46
Why is Hastings-Metropolis ? On the off chance that we characterize Can see that for Gibbs: When an is dependably 1Slide 47
Simulated AnnealingSlide 48
Simulated Annealing Goal: Find (surmised) least of some positive capacity Function characterized on a to a great degree substantial number of states, s And to discover those states where this capacity is minimized Value of the capacity for state is:Slide 49
Simulated Annealing Process Construct neighborhood of every state Set of states "close" to the state Variable in Markov bind can move to a neighbor in one stage Moves outside neighborhood not permittedSlide 50
Simulated Annealing Requirements of neighborhood If is in neighborhood of then is in the area of Number of states in an area (N) is free of that state Neighborhoods are connected so chain can inevitably make it from any E j to any E m . In the event that in state E j , then the following move must be in neighborhood of E j .Slide 51
Simulated Annealing Uses a positive parameter T Aim is to have the stationary appropriation of every Markov chain state being: Constant to guarantee entirety of probabilities is 1 Visit frequently enough to permit those states with low estimation of f() to end up unmistakableSlide 52
Simulated AnnealingSlide 53
Simulated Annealing Large T values All states in current states neighborhood are picked with ~ approach likelihood Stationary dissemination of tie has a tendency to be uniform Small T values Different states in neighborhoods have vastly different stationary dispersion probabilities Too little may stall out in nearby maximaSlide 54
Simulated Annealing Art of picking T esteem Want quick development starting with one neighborhood then onto the next (Large T) Picks out states in neighborhoods with extensive stationary probabilities (Small T)Slide 55
SA ExampleSlide 56
Absorbing Markov ChainsSlide 57
Absorbing Markov Chains Absorbing state: State which is difficult to leave p ii = 1 Transient state: Non-retaining state in engrossing chainSlide 58
Absorbing Markov Chains Questions to reply: Given chain begins at a specific state, what is the normal number of ventures before being consumed? Given chain begins at a specific state, what is the likelihood it will be consumed by a specific engrossing state?Slide 59
General Process Use Explanation from Introduction to Probability – Grinstead Convert network into standard shape Uses transformations to answer these inquiries Use basic case all throughSlide 60
Canonical Form Rearrange states so that the transient states start things out in P t x t framework t x r grid t : # of transient states r x t zero lattice r x r character network r : # of engrossing statesSlide 61
Drunkard\'s Walk Example Man strolling home from a bar 4 squares to walk 5 states add up to Absorbing states: Corner 4 – Home Corner 0 – Bar Each piece he has an equivalent likelihood of going ahead or in reverseSlide 62
Drunkard\'s Walk ExampleSlide 63
Drunkard\'s Walk : Canonical Form Canonical frameSlide 64
Fundamental Matrix For a retaining Markov Chain P Fundamental Matrix for P is: n ij section gives expected number of times that the procedure is in the transient state s j if began in transient state s i (Befo
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