On 13 Junea week after the allied invasion of Normandy, a loud buzzing sound rattled through the skies of battle-worn London. The source of the sound was a newly developed German instrument of war, the V-1 flying bomb. A precursor to the cruise missile, the V-1 was a self-propelled flying bomb, guided using gyroscopes, and powered by a simple pulse jet engine that gulped air and ignited fuel 50 times a second.

This high frequency pulsing gave the bomb its characteristic soundearning them the nickname buzzbombs. From June to Octoberthe Germans launched 9, buzzbombs from the coasts of France and the Netherlands, of which 2, reached their targets in London.

The British worried about the accuracy of these aerial drones. Were they falling haphazardly over the city, or were they hitting their intended targets? Had the Germans really worked out how to make an accurately targeting self-guided bomb?

Fortunately, they were scrupulous in maintaining a bomb censusthat tracked the place and time of nearly every bomb that was dropped on London during World War II. With this data, they could statistically ask whether the bombs were falling randomly over London, or whether they were targeted. This was a math question with very real consequences. Imagine, for a moment, that you are working for the British intelligence, and you're tasked with solving this problem.

Someone hands you a piece of paper with a cloud of points on it, and your job is to figure out if the pattern is random.

Let's make this more concrete. One of the patterns is randomly generated. The other imitates a pattern from nature. Can you tell which is which? The one on the left, with the clumps, strands, voids, and filaments and perhaps, depending on your obsessions, animals, nudes, or Virgin Marys is the array that was plotted at random, like stars. The one on the right, which seems to be haphazard, is the array whose positions were nudged apart, like glowworms.

That's right, glowworms. The points on the right records the positions of glowworms on the ceiling of the Waitomo cave in New Zealand. These glowworms aren't sitting around at random, they're competing for food, and nudging themselves away from each other. They have a vested interest against clumping together. Update: Try this out for yourself.

### What does randomness look like?

After reading this article, praptak and roryokane over at hacker news wrote a script that will generate random and uniform distributions in your browser, nicely illustrating the point. Try to uniformly sprinkle sand on a surface, and it might look like the pattern on the right.Kelton, who researches the probabilistic and statistical aspects of simulation, is chair of Smeal College's Department of Management Science and Information Systems.

Reasonable and realistic physical assumptions imply that the time between successive events follows what is called an exponential distribution, the most likely value of which is zero or arbitrarily close to zero. This process, Kelton points out, was discovered by the French mathematician and probabilist Poisson, who was consulting with the Prussian army to explain why so may Prussian army officers were being killed by getting kicked by horses.

Note: Content may be edited for style and length. Science News. ScienceDaily, 23 August Retrieved October 8, from www. The University of Florida's International Shark Attack File reported 81 unprovoked attacks worldwide, in line with the Emergent gravity, as the new theory is called, predicts the exact same deviation of motions that is usually explained So far its extinction has been explained with the onset of an ice age. However, researchers have ScienceDaily shares links with sites in the TrendMD network and earns revenue from third-party advertisers, where indicated.

Living Well. View all the latest top news in the environmental sciences, or browse the topics below:. Keyword: Search.Nothing quite racks havoc on an idealistic summer swim off the Florida Keys quite like a shark attack. Where would you look for the data? More importantly, which algorithm would a Data Scientist choose to predict the results and why?

The Florida Museum of Natural History had maintained the International Shark Attack File, a record of unprovoked shark attacks and the resulting fatalities worldwide dating back to Poisson Regression, also referred to a log-linear model when working with categorical data, is now common in most analytical packages and is recommended wherever you need to model count data or construct contingency tables.

This model for example could be used profitably to predict retweets of Twitter data, or failures of nuclear plants under various operating conditions, or then again to predict exam success rates among identifiable groups of students. What are the assumptions of this model?

Poisson regression is similar to logistic regression in that it also has a discrete response variable. The model assumes the logarithm of its expected value can be modeled by a linear combination of unknown parameters.

The recorded events occur with a known constant rate and independently of the time since previous events. More importantly, the model assumes that the expected value the response variable has a Poisson, rather than normal, distribution.

In other words, the possible values of the response variable will be nonnegative integers like 0, 1, 2, 3, etc. What are the use cases for Poisson regression?

## Shark Attack — explaining the use of Poisson regression

The model can be used profitably in stochastic processes where the observable events occur randomly over time. There should be little, if any, chance that two or more occurrences of the event could transpire in each interval. Accepting these assumptions, we can argue that the probability distribution of the number of occurrences of the event in a fixed time distance, area, or volume interval conforms to a Poisson distribution.

What precautions should we take when using the Poisson regression model? Three potential characteristics of the test population come to mind. As a Data Scientist, you should be wary of the potential for heterogeneity in the data — is there more than one process generating expected values? Overdispersion is a second anomaly that needs to be accounted for — is the variance of the fitted model larger than what could be expected by your assumptions?

Finally, does the data sample reflect underdispersion — i. In the suggested case of shark attacks, the choice of the Poisson model is justified by the relative rarity of such horrid events. Dividing fitted means by population size yields a model for rates. To improve the accuracy of the model, we can introduce an offset term for the log of the population size in the population equation.

As a result, the predicted time trend follows an exponential pattern rather than linear due to the use of the log link. The practice of business analytics is the heart and soul of the Business Analytics Institute.

In our Summer School in Bayonne, as well as in our Master Classes in Europe, the Business Analytics Institute focuses on digital economics, data-driven decision making, machine learning, and visual communications will put analytics to work for you and your organization.

His LinkedIn profile can be viewed at www. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Make learning your daily ritual. Take a look.Nothing quite racks havoc on an idealistic summer swim off the Florida Keys quite like a shark attack. Where would you look for the data? More importantly, which algorithm would a Data Scientist choose to predict the results and why?

The Florida Museum of Natural History had maintained the International Shark Attack File, a record of unprovoked shark attacks and the resulting fatalities worldwide dating back to Poisson Regression, also referred to a log-linear model when working with categorical data, is now common in most analytical packages and is recommended wherever you need to model count data or construct contingency tables. This model for example could be used profitably to predict retweets of Twitter data, or failures of nuclear plants under various operating conditions, or then again to predict exam success rates among identifiable groups of students.

What are the assumptions of this model? Poisson regression is similar to logistic regression in that it also has a discrete response variable. The model assumes the logarithm of its expected value can be modeled by a linear combination of unknown parameters.

The recorded events occur with a known constant rate and independently of the time since previous events. More importantly, the model assumes that the expected value the response variable has a Poisson, rather than normal, distribution. In other words, the possible values of the response variable will be nonnegative integers like 0, 1, 2, 3, etc.

What are the use cases for Poisson regression? The model can be used profitably in stochastic processes where the observable events occur randomly over time.

There should be little, if any, chance that two or more occurrences of the event could transpire in each interval. Accepting these assumptions, we can argue that the probability distribution of the number of occurrences of the event in a fixed time distance, area, or volume interval conforms to a Poisson distribution. What precautions should we take when using the Poisson regression model?

Three potential characteristics of the test population come to mind. As a Data Scientist, you should be wary of the potential for heterogeneity in the data — is there more than one process generating expected values? Overdispersion is a second anomaly that needs to be accounted for — is the variance of the fitted model larger than what could be expected by your assumptions? Finally, does the data sample reflect underdispersion — i.Poisson clumpingor Poisson bursts[1] is the phenomenon wherein random events may appear to have a tendency to occur in clusters, clumps, or bursts.

The Poisson process provides a description of random independent events occurring with uniform probability through time or space or both. Poisson clumping is used to explain marked increases or decreases in the frequency of an event, such as shark attacks, "coincidences", birthdays, or heads or tails from coin tosses, and e-mail correspondence.

Poisson clumping heuristic PCHpublished by David Aldous in[5] is a model for finding first-order approximations over different areas in a large class of stationary probability models that have a specific monotonicity property for large exclusions. The probability that such a process will achieve a large value is asymptotically small and is distributed in a Poisson fashion.

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### Poisson clumping

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Aquarius Monthly Horoscope This continues to be a favourable period for you dear Aquarius. The sun remains in Scorpio lending personal magnetism and impact to your character.Example: true tags optional A list of strings that help classify and retrieve the deepnet.

Example: "000005" beta1 optional A number between 0 and 1 specifying the exponential decay rate for the 1st moment estimates. Used in the adam algorithm. Example: 2 decay optional A number between 0 and 1 specifying the decay computation. Used in the ftrl and adagrad algorithms. Used in the ftrl algorithm. Example: "l1" category filterable, sortable, updatable One of the categories in the table of categories that help classify this resource according to the domain of application.

This will be 201 upon successful creation of the deepnet and 200 afterwards. Make sure that you check the code that comes with the status attribute to make sure that the deepnet creation has been completed without errors.

This is the date and time in which the deepnet was created with microsecond precision. True when the deepnet has been created in the development mode. The list of fields's ids that were excluded to build the models of the deepnet.

Provides a measure of how important an input field is relative to the others to predict the objective field. Each field is normalized to take values between zero and one. The list of input fields' ids used to build the models of the deepnet.

Specifies the id of the field that the deepnet predicts. In a future version, you will be able to share deepnets with other co-workers or, if desired, make them publicly available. This is the date and time in which the deepnet was updated with microsecond precision. A number between 0 and 1 specifying the rate at which to drop weights during training to control overfitting. A dictionary with an entry per field in the dataset used to build the deepnet.

Whether alternate layers should learn a representation of the residuals for a given layer rather than the layer itself or not. Complete information of the network. The key is the name of the algorithm used.

Whether to learn a tree-based representation of the data as engineered features along with the raw features, essentially by learning trees over slices of the input space and a small amount of the training data. Each layer is a map, and its structure will vary depending on the structure of the layers.

**5 SHARK ATTACKS On Baywatch! Baywatch Remastered**

This includes per-node class names for classification problems and distribution information of the objective for regression problems. A list of maps, each one of which is a preprocessor, specifying one input feature to the network. This layer may comprise binary encoding, normalization, and feature selection, as there may be less preprocessors than features in the original data. A status code that reflects the status of the deepnet creation.

Number of milliseconds that BigML took to process the deepnet. Example: 1 combiner optional Specifies the method that should be used to combine predictions in a non-boosted ensemble. For classification ensembles, the combination is made by majority vote. The options are: 0: plurality weights each model's prediction as one vote. You can set up both using the threshold argument. If there are less than k models voting class, the most frequent of the remaining categories is chosen, as in a plurality combination after removing the models that were voting for class.

The confidence of the prediction is computed as that of a plurality vote, excluding votes for the majority class when it's not selected.