Bayesian Search Models

March 13, 2014
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(This article was first published on jacobsimmering.com, and kindly contributed to R-bloggers)

Bayesian Search Theory

The US had a pretty big problem on their hands in 1966. Two planes had hit each other during a in-flight refueling and crashed. Normally, this would be an unfortunate thing and terrible for the families of those involved in the crash but otherwise fairly limited in importance. However, in this case, the plane being refueled was carrying four hydrogen bombs.

Of the four bombs, only three were found on land. Two had their non-nuclear payload explode and the third was intact and embedded in a river bank. As a result, the fourth bomb was thought to be in the sea. A local man reported that he saw the bomb enter the water and that was basically all of the information that was available to find the bomb.

The Navy started a search using Bayesian search theory. Bayesian search theory recognizes that there are two factors that contribute to the probability of finding a lost item in a given location: the probability that the object is in a given location and the probability of locating the object given that it is in the searched location. When looking for objects in difficult places, such as under water or in rough terrain, it may be very difficult to find an object even if it exists.

Bayesian search theory starts by by expressing the probability that an object \( O \) will be found in a location \( (x, y) \) is as by the product \( P(O) \) and \( P(D | O) \) where \( D \) is the event of detection of the object \( O \).

This makes sense intuitively. For instance, the probability of me finding a scarp of paper is smaller in my office which is overflowing with scraps of paper than in my kitchen despite the fact that my office is much more likely to contain the scrap of paper.

To use Bayesian search theory to find an object, you would calculate the probability that the object will be found in each location on the basis of the detection probability and the prior probability. You would then search first in the location most likely to result in you successfully finding the object. Assuming you don't find the object in that location, you can update the probability of finding that object by iteratively updating the probability with Bayes theorem.

To make this concrete, suppose we have a particle in a 2D box. Let the box be 61 units by 61 units wide and divided into 3721 “cells” of 1 square unit. Suppose that during each time-step, the particle draws a move for each x and y from a multivariate normal centered at zero with variance of 1 and covariate of 0. (Although the grid is divided into boxes, the particle is not limited to discrete values). After \( t \) time steps, the pdf for the location of the particle is simply \( N(\mathbf{\mu}, t\mathbf{\Sigma}) \) as it is the result of the summation of a set of independent normal RV. We will transform this to the the grid instead of using contours. If we let the particle “walk” for 100 time steps, the resulting pmf/pdf looks like

d <- data.frame(x = rep(seq(-30, 30), each = 61), y = rep(seq(-30, 30), times = 61))
d$PrP <- dnorm(d$x, 0, sqrt(100)) * dnorm(d$y, 0, sqrt(100))
ggplot(d, aes(x = x, y = y, z = PrP)) + geom_point(aes(alpha = PrP)) + stat_contour()

As you might expect as \( N(0, 100) \) distribution is fairly flat.

Now lets come up with a model for \( P(D | O) \). In the case of the Navy's search, this was a function of depth in that grid location. For simplicity, I'm going to code detection probability as a function of distance from a given point. I placed a detector station at \( (10, 10) \) and in that grid square the detector has a 97.5% chance of detecting the particle if it is there. The detection probability decays with distance. For this, I simply modeled as it \( 0.975 * 0.925^d \) where \( d \) is the shortest linear distance to the detector.

The detection power then looks something like

detectionPower <- function(x, y, dx = 10, dy = 5, p0 = 0.975, d = 0.925) {
    x2 <- x - dx
    y2 <- y - dx
    r <- sqrt(x2^2 + y2^2)
    power <- p0 * d^r
}
d$PrD <- detectionPower(d$x, d$y)
ggplot(d, aes(x = x, y = y, z = PrD)) + geom_point(aes(alpha = PrD)) + stat_contour(binwidth = 0.1)

The product of these two functions is proportional to the probability that a single search of an grid will find the particle. This is shown below

d$valueOfSearch <- d$PrP * d$PrD
ggplot(d, aes(x = x, y = y, z = d$valueOfSearch)) + geom_point(aes(alpha = d$valueOfSearch)) + 
    stat_contour()

Putting all three graphics together

nd <- data.frame(x = rep(d$x, 3), y = rep(d$y, 3), value = c(d$PrP, d$PrD, d$valueOfSearch), 
    metric = gl(3, nrow(d), labels = c("PDF of Object", "Detection Prob", "Value of Search")))
ggplot(nd, aes(x = x, y = y, z = value)) + stat_contour() + facet_grid(. ~ metric) + 
    scale_x_continuous(limits = c(-30, 30)) + scale_y_continuous(limits = c(-30, 
    30))

it is clear that the optimal areas to search are actually pulled slightly away from the most probable areas because those areas are less likely to be fruitful given the reduced values of \( P(D | O) \).

Suppose in a given time unit, 100 areas can be searched using the allotted budget and the particle is no longer moving. On the first day, it would be optimal to search the 100 most likely spots

If the object is not found in the searched spots, the probability of finding the object in that spot, by application of Bayes Theorem, becomes

\[ P(O) = \frac{P(O)_0 (1 - P(D | O))}{(1 - P(O)_0 + P(O)_0 (1 - P(D|O)))} \]

And for the locations not searched, their probability is also revised upwards (just like when the Monty Hall opens a door) according to

\[ P(O) = \frac{P(O)_0}{(1 - P(O)_0 + P(O)_0 (1 - P(D|O)))} \]

Suppose that we search the first 100 locations, what does the updated search value look like?

bayesUpdate <- function(searched, p0, pD) {
    (p0 * (1 - searched * pD))/(1 - p0 + p0 * (1 - pD))
}
d$searched <- rank(-1 * d$valueOfSearch) <= 100
d$newSearchValue <- bayesUpdate(d$searched, d$PrP, d$PrD)
nd <- data.frame(x = rep(d$x, 2), y = rep(d$y, 2), valueOfSearch = c(d$valueOfSearch, 
    d$newSearchValue), searched = rep(d$searched, 2), search = rep(c("Before Any Searching", 
    "First Wave"), each = nrow(d)))
nd$searched[nd$searched == FALSE] <- NA
ggplot(nd, aes(x = x, y = y, z = valueOfSearch)) + stat_contour() + facet_grid(. ~ 
    search) + geom_point(aes(color = searched, alpha = valueOfSearch))

The searched area becomes less probable than before, however, it does not go to zero after a single search. Additionally, points with higher prior probabilities of having the object remain relatively likely depsite having being searched. Furthermore, the whole distribution moved as a result of not finding the object in the first 100 locations.

Using these new probabilities, you could update your search path and search the next 100 locations and so on until either the object is found or the probability of ever finding it in the search area is nearly zero.

searchCount <- rep(0, nrow(d))
probInSearchArea <- numeric(1000)
probFindingInGrid <- numeric(1000)
p0 <- d$PrP * d$PrD
pD <- d$PrD
for (i in 1:1000) {
    searchLocations <- rank(-1 * p0) <= 100
    searchCount <- searchCount + searchLocations
    probInSearchArea[i] <- sum(p0[searchLocations])
    probFindingInGrid[i] <- sum(p0)
    p0 <- bayesUpdate(searchLocations, p0, pD)
}
nSearches <- data.frame(x = d$x, y = d$y, count = searchCount)
ggplot(nSearches, aes(x = x, y = y, z = count)) + stat_contour() + geom_point(aes(alpha = count))

This result makes sense. The areas most distant from the detection center required the most searching as the areas closer can be more quickly ruled out due to the greater values of \( P(D|O) \).

Since each grid location is 1 square unit, the sum of the probabilities for each unit is approximately equal to the integral of the pdf over the 31 x 31 search area. Exploiting this, we can easily calculate the probability of finding the object with the sum of the probabilities at each point. (This approximates the integral fairly well as the grid is 1 unit square which is fairly small compared to the variance and total grid size).

searchValue <- data.frame(searchNumber = 1:1000, marginalvalue = probInSearchArea, 
    cumulativeValue = probFindingInGrid)
ggplot(searchValue, aes(x = searchNumber)) + geom_line(aes(y = marginalvalue)) + 
    geom_line(aes(y = probFindingInGrid), lty = 2) + scale_y_continuous("Probability of Finding the Object") + 
    scale_x_continuous("Number of Unsuccessful Searches")

The probability of finding the object in the next search or within the considered grid decreases as the number of failed searches increases. This is one of the greatest features of Bayesian search theory: by estimating the probability of ever finding the object given the search area and previous efforts, you can determine when searching is no longer economically (or otherwise) viable.

Additionally, by adjusting resources according to new information, such as the failure to find the object in a searched sector, Bayesian methods reduce the time required to find an object, especially when the \( P(D | O) \) is highly variable. With some assumptions in the modeling, a Bayesian search model found the end of a random walk in fewer steps than a search model based only on the pdf of the random walk or a model based on the product of the random walk pdf and the detection probability. You can see the complete code for these simulations here.

By combining the relevant information from prior searching and the likelihood of a successful search given the object is located in the area, Bayesian search theory provides an method for the optimal deployment of search resources. Now, off to figure out where I put my iPad.

To leave a comment for the author, please follow the link and comment on his blog: jacobsimmering.com.

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