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Sequential Detection Overview & Open Problems George V. Moustakides, University of Patras, GREECE.

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Presentation on theme: "Sequential Detection Overview & Open Problems George V. Moustakides, University of Patras, GREECE."— Presentation transcript:

1 Sequential Detection Overview & Open Problems George V. Moustakides, University of Patras, GREECE

2 Outline  Sequential hypothesis testing  SPRT  Application from databases  Open problems  Sequential detection of changes  The Shiryaev test  The Shiryaev-Roberts test  The CUSUM test  Open problems  Decentralized detection (sensor networks) GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 2

3 Sequential Hypothesis testing Conventional binary hypothesis testing: Fixed sample size observation vector X=[x 1,...,x K ] X satisfies the following two hypotheses: GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 Given the data vector X, decide between the two hypotheses. 3 Decision rule:

4 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 Bayesian formulation Likelihood ratio test: 4 Neyman-Pearson formulation For i.i.d.:

5 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 Observations x 1,...,x t,... become available sequentially 5 Sequential binary hypothesis testing Time Observations Decision Decide Reliably Decide as soon as possible!

6 Yes No No GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 6 Time Observations We apply a two-rule procedure 1 st rule: at each time instant t, evaluates whether the observed data can lead to a reliable decision Can x 1 lead to a reliable decision? Can x 1,x 2 lead to a reliable decision? Can x 1,x 2,..., x T lead to a reliable decision? STOP getting more data. 2 nd rule: Familiar decision rule T is a stopping rule Random!

7 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 7 Why Sequential ? In average, we need significantly less samples to reach a decision than the fixed sample size test, for the same level of confidence (same error probabilities) For the Gaussian case it is 4 - 5 times less samples.

8 SPRT (Wald 1945) GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 8 Here there are two thresholds A < 0 < B Stopping rule: Decision rule:

9 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 9 utututut t B A 0 T H0H0H0H0 H1H1H1H1 Infinite Horizon

10 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 10 Amazing optimality property!!! SPRT solves both problems simultaneously A,B need to be selected to satisfy the two error probability constraints with equality  I.i.d. observations (1948, Wald-Wolfowitz)  Brownian motion (1967, Shiryaev)  Homogeneous Poisson (2000, Peskir-Shiryaev) Open Problems: Dependency, Multiple Hypotheses

11 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 11 utututut B A 0 H0H0H0H0 H1H1H1H1 T t K Finite Horizon

12 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 12 attribute 1 attribute 2 attribute 3... attribute K... Data Base (records)... 0011 Record linkage x1x1x1x1 x2x2x2x2 x3x3x3x3 xKxKxKxK We assume known probabilities for x i =0 or 1 under match (Hypothesis H 1 ) or nonmatch (Hypothesis H 0 ) for each attribute.

13 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 13 Data from: Statistical Research Division of the US Bureau of the Census

14 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 14 Total number of record comparisons: 3703

15 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 15 Total number of record comparisons: 3703

16 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 16 In collaboration with V. Verykios

17 Sequential change detection GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 17t¿ Change in statistics Detect occurrence as soon as possible xtxtxtxt

18 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 18 Applications Monitoring of quality of manufacturing process (1930’s) Biomedical Engineering Electronic Communications Econometrics Seismology Speech & Image Processing Vibration monitoring Security monitoring (fraud detection) Spectrum monitoring Scene monitoring Network monitoring and diagnostics (router failures, intruder detection) Databases.....

19 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 19 Mathematical setup We observe sequentially a process { x t } that has the following statistical properties  Changetime ¿ : either random with known prior or deterministic but unknown.  Both pdfs f 0, f 1 are considered known. Detect occurrence of ¿ as soon as possible

20 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 20 We are interested in sequential detection schemes. Whatever sequential scheme one can think of, at every time instant t it will have to make one of the following two decisions:  Either decide that a change didn’t take place before t, therefore it needs to continue taking more data.  Or that a change took place before t and therefore it should stop and issue an alarm. Sequential Detector  Stopping rule

21 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 21 Shiryaev test (Bayesian, Shiryaev 1963) Changetime ¿ is random with Geometric prior. Optimum T : If T is a stopping rule then we define the following cost function

22 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 22 Define the statistic: There exists such that the following rule is optimum. In discrete time when { x t } are i.i.d. before and after the change. In continuous time when { x t } is a Brownian motion with constant drift before and after the change. In continuous time when { x t } is Poisson with constant rate before and after the change.

23 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 23 Shiryaev-Roberts test (Minmax, Pollak 1985) Changetime ¿ is deterministic and unknown. For any stopping rule T define the following criterion: Optimum T : subject to

24 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 24 In discrete time, when data are i.i.d. before and after the change with pdfs f 0, f 1. Compute recursively the following statistic: Yakir (AoS 1997) provided a proof of strict optimality. Mei (2006) showed that the proof was problematic. Pollak (1985) Tartakovsky (2011) found a counterexample.

25 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 25 CUSUM test (minmax, Lorden 1971) Changetime ¿ is deterministic and unknown. For any stopping rule T define the following criterion: Optimum T : subject to

26 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 26 Compute the Cumulative Sum (CUSUM) statistic y t as follows: Running LLR Running minimum For i.i.d. CUSUM test CUSUM statistic

27 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 27 utut mtmt S º º º

28 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 28 Moustakides (1986) strict optimality. Continuous time Shiryaev (1996), Beibel (1996) strict optimality for BM Moustakides (2004) strict optimality for Ito processes Moustakides (>2012) strict optimality for Poisson processes. Open Problems: Dependent data, Non abrupt changes, Transient changes,… Discrete time: i.i.d. before and after the change Lorden (1971) asymptotic optimality.

29 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 29 Decentralized detection

30 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 30 t B1B1B1B1 A1A1A1A1 0 0 1 utututut1 t1t1t1t1 t2t2t2t2 utututut1 utututut1 1 - If more than 1 bits, quantize overshoot!

31 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 31 The Fusion center if at time t n receives information from sensor i it updates an estimate of the global log- likelihood ratio: and performs an SPRT (if hypothesis testing) or a CUSUM (if change detection) using the estimate of the global log-likelihood ratio.

32 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 32

33 GV MOUSTAKIDES: Sequential Detection, Overview & Open Problems, ISR, Dec. 2011 33 Thank you for your attention

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