The full formula also includes an error term to account for random sampling noise. Not strictly an answer but when you flip a coin three times and head comes up two times then no student would believe, that head was twice as likely as tails.That is pretty convincing although certainly not real research. I'll use the data set airquality within R. Consider the problem of estimating average wind speeds (MPH). So, if you were to bet on the winner of next race, who would he be ? Identifying a weighted coin. The American Statistician, 39(2), 83-87. How do EMH proponents explain Black Monday (1987)? The Bayesian approach can be especially used when there are limited data points for an event. Recent developments in Markov chain Monte Carlo (MCMC) methodology facilitate the implementation of Bayesian analyses of complex data sets containing missing observations and multidimensional outcomes. What Bayes tells us is. It only takes a minute to sign up. This doesn't take into account the uncertainty of $\beta$. Comparing a Bayesian model with a Classical model for linear regression. I would like to give students some simple real world examples of researchers incorporating prior knowledge into their analysis so that students can better understand the motivation for why one might want to use Bayesian statistics in the first place. Journal of the American Statistical Association. 1. We tell this story to our students and have them perform a (simplified) search using a simulator. The Normal distribution is conjugate to the Normal distribution. The idea is to see what a positive result of the urine dipslide imply on the diagnostic of urine infection. Or as more typically written by Bayesian, $$ In this experiment, we are trying to determine the fairness of the coin, using the number of heads (or tails) that … There is no correct way to choose a prior. $$, where $\tau = 1 / \sigma^2$; $\tau$ is known as the precision, With this notation, the density for $y_i$ is then, $$ Here the vector $y = (y_1, ..., y_n)^T$ represents the data gathered. Our goal in developing the course was to provide an introduction to Bayesian inference in decision making without requiring calculus, with the book providing more details and background on Bayesian Inference. Most important of all, we offer a number of worked examples: Examples of Bayesian inference calculations General estimation problems. Bayesian statistics, Bayes theorem, Frequentist statistics. If you already have cancer, you are in the first column. •Example 1 : the probability of a certain medical test being positive is 90%, if a patient has disease D. 1% of the population have the disease, and the test records a false positive 5% of the time. By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy. rev 2020.12.2.38097, The best answers are voted up and rise to the top, Cross Validated works best with JavaScript enabled, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site, Learn more about Stack Overflow the company, Learn more about hiring developers or posting ads with us. This is where Bayesian … The Mathematics Behind Communication and Transmitting Information, Solving (mathematical) problems through simulations via NumPy, Manifesto for a more expansive mathematics curriculum, How to Turn the Complex Mathematics of Vector Calculus Into Simple Pictures, It excels at combining information from different sources, Bayesian methods make your assumptions very explicit. with $H+$ the hypothesis of a urine infection, and $H-$ no urine infection. Bayesian search theory is an interesting real-world application of Bayesian statistics which has been applied many times to search for lost vessels at sea. Bayesian Statistics is about using your prior beliefs, also called as priors, to make assumptions on everyday problems and continuously updating these beliefs with the data that you gather through experience. A choice of priors for this Normal data model is another Normal distribution for $\theta$. Say you wanted to find the average height difference between all adult men and women in the world. You are now almost convinced that you saw the same person. if the physician estimate that this probability is $p_{+} = 2/3$ based on observation, then a positive test leads the a post probability of $p_{+|test+} = 0.96$, and of $p_{+|test-} = 0.37$ if the test is negative. MathJax reference. Which statistical software is suitable for teaching an undergraduate introductory course of statistics in social sciences? The (admittedly older) Frequentist literature deals with a lot of these issues in a very ad-hoc manner and offers sub-optimal solutions: "pick regions of $x$ that you think should lead to both 0's and 1's, take samples until the MLE is defined, and then use the MLE to choose $x$". The term Bayesian statistics gets thrown around a lot these days. $$. $$, Classical statistics (i.e. You update the probability as 0.36. In Bayesian statistics, you calculate the probability that a hypothesis is true. 2. The probability model for Normal data with known variance and independent and identically distributed (i.i.d.) You also obtain a full distribution, from which you can extract a 95% credible interval using the 2.5 and 97.5 quantiles. Say, you find a curved surface on one edge and a flat surface on the other edge, then you could give more probability to the faces near the flat edges as the die is more likely to stop rolling at those edges. The article describes a cancer testing scenario: 1. An alternative analysis from a Bayesian point of view with informative priors has been done by (Downey, 2013), and with an improper uninformative priors by (Höhle and Held, 2004). Ask yourself, what is the probability that you would go to work tomorrow? How to avoid boats on a mainly oceanic world? 80% of mammograms detect breast cancer when it is there (and therefore 20% miss it). Thanks for contributing an answer to Cross Validated! Boca Raton, Fla.: Chapman & Hall/CRC. This differs from a number of other interpretations of probability, such as the frequentist interpretation that views probability as the limit of the relative frequency of an What is the probability that it would rain this week? Does a regular (outlet) fan work for drying the bathroom? Bayesian Statistics partly involves using your prior beliefs, also called as priors, to make assumptions on everyday problems. Mathematical statistics uses two major paradigms, conventional (or frequentist), and Bayesian. Gelman, A. You change your reasoning about an event using the extra data that you gather which is also called the posterior probability. The Bayes’ theorem is expressed in the following formula: Where: 1. As per this definition, the probability of a coin toss resulting in heads is 0.5 because rolling the die many times over a long period results roughly in those odds. Casella, G. (1985). Here's a simple example to illustrate some of the advantages of Bayesian data analysis over maximum likelihood estimation (MLE) with null hypothesis significance testing (NHST). So, you collect samples … Consider a random sample of n continuous values denoted by $y_1, ..., y_n$. One Sample and Pair Sample T-tests The Bayesian One Sample Inference procedure provides options for making Bayesian inference on one-sample and two-sample paired t … $$. If you do not proceed with caution, you can generate misleading results. In this analysis, the researcher (you) can say that given data + prior information, your estimate of average wind, using the 50th percentile, speeds should be 10.00324, greater than simply using the average from the data. Bayesian Statistics Interview Questions and Answers 1. Let’s assume you live in a big city and are shopping, and you momentarily see a very famous person. Clearly, you don't know $\beta$ or you wouldn't need to collect data to learn about $\beta$. One can show that for a given $\beta$ there is a set of $x$ values that optimize this problem. You assign a probability of seeing this person as 0.85. The posterior belief can act as prior belief when you have newer data and this allows us to continually adjust your beliefs/estimations. P (seeing person X | personal experience, social media post, outlet search) = 0.36. You can check out this answer, written by yours truly: Are you perhaps conflating Bayes Rule, which can be applied in frequentist probability/estimation, and Bayesian statistics where "probability" is a summary of belief? This article intends to help understand Bayesian statistics in layman terms and how it is different from other approaches. O'Reilly Media, Inc.", 2013. Starting with version 25, IBM® SPSS® Statistics provides support for the following Bayesian statistics. When we flip a coin, there are two possible outcomes — heads or tails. In a Bayesian perspective, we append maximum likelihood with prior information. How to tell the probability of failure if there were no failures? The current world population is about 7.13 billion, of which 4.3 billion are adults. For example, we can calculate the probability that RU-486, the treatment, is more effective than the control as the sum of the posteriors of the models where \(p<0.5\). Bayesian methods provide a complete paradigm for both statistical inference and decision mak-ing under uncertainty. Additionally, each square is assigned a conditional probability of finding the vessel if it's actually in that square, based on things like water depth. The posterior precision is $b + n\tau$ and mean is a weighted mean between $a$ and $\bar{y}$, $\frac{b}{b + n\tau} a + \frac{n \tau}{b + n \tau} \bar{y}$. Let me explain it with an example: Suppose, out of all the 4 championship races (F1) between Niki Lauda and James hunt, Niki won 3 times while James managed only 1. If Jedi weren't allowed to maintain romantic relationships, why is it stressed so much that the Force runs strong in the Skywalker family? How to animate particles spraying on an object. Where $OR$ is the odds ratio. Since you live in a big city, you would think that coming across this person would have a very low probability and you assign it as 0.004. You could just use the MLE's to select $x$, but, This doesn't give you a starting point; for $n = 0$, $\hat \beta$ is undefined, Even after taking several samples, the Hauck-Donner effect means that $\hat \beta$ has a positive probability of being undefined (and this is very common for even samples of, say 10, in this problem). You can incorporate past information about a parameter and form a prior distribution for future analysis. Bayesian Probability in Use. maximum likelihood) gives us an estimate of $\hat{\theta} = \bar{y}$. Below I include two references, I highly recommend reading Casella's short paper. What if you are told that it raine… From a practical point of view, it might sometimes be difficult to convince subject matter experts who do not agree with the validity of the chosen prior. Holes in Bayesian Statistics Andrew Gelmany Yuling Yao z 11 Feb 2020 Abstract Every philosophy has holes, and it is the responsibility of proponents of a philosophy to point out these problems. Bayesian statistics tries to preserve and refine uncertainty by adjusting individual beliefs in light of new evidence. However, in this particular example we have looked at: 1. So, you start looking for other outlets of the same shop. The probability of an event is measured by the degree of belief. How to estimate posterior distributions using Markov chain Monte Carlo methods (MCMC) 3. Most problems can be solved using both approaches. Bayesian statistics uses an approach whereby beliefs are updated based on data that has been collected. For example, I could look at data that said 30 people out of a potential 100 actually bought ice cream at some shop somewhere. The next day, since you are following this person X in social media, you come across her post with her posing right in front of the same store. Here the prior knowledge is the probability to have a urine infection based on the clinical analysis of the potentially sick person before making the test. The work by (Höhle and Held, 2004) also contains many more references to previous treatment in the literature and there is also more discussion of this problem on this site. It's specifically aimed at empirical Bayes methods, but explains the general Bayesian methodology for Normal models. An introduction to the concepts of Bayesian analysis using Stata 14. Now, you are less convinced that you saw this person. This is commonly called as the frequentist approach. These include: 1. samples is, $$ Of course, there is a third rare possibility where the coin balances on its edge without falling onto either side, which we assume is not a possible outcome of the coin flip for our discussion. Before delving directly into an example, though, I'd like to review some of the math for Normal-Normal Bayesian data models. They want to know how likely a variant’s results are to be best overall. Now you come back home wondering if the person you saw was really X. Let’s say you want to assign a probability to this. Will I contract the coronavirus? Thomas Bayes(1702‐1761) BayesTheorem for probability events A and B Or for a set of mutually exclusive and exhaustive events (i.e. Let’s try to understand Bayesian Statistics with an example. Bayesian inference is an important technique in statistics, and especially in mathematical statistics.Bayesian updating is particularly important in the dynamic analysis of a sequence of data. $$ Chapter 3, Downey, Allen. This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. https://www.quantstart.com/articles/Bayesian-Statistics-A-Beginners-Guide P(B|A) – the probability of event B occurring, given event A has occurred 3. Similar examples could be constructed around the story of the lost flight MH370; you might want to look at Davey et al., Bayesian Methods in the Search for MH370, Springer-Verlag. Here is an example of estimating a mean, $\theta$, from Normal continuous data. Do MEMS accelerometers have a lower frequency limit? Ultimately, the area of Bayesian statistics is very large and the examples above cover just the tip of the iceberg. Would you measure the individual heights of 4.3 billion people? In addition, your estimate of $\theta$ in this model is a weighted average between the empirical mean and prior information. An area of research where I believe the Bayesian methods are absolutely necessary is that of optimal design. This book was written as a companion for the Course Bayesian Statistics from the Statistics with R specialization available on Coursera. For example, if we have two predictors, the equation is: y is the response variable (also called the dependent variable), β’s are the weights (known as the model parameters), x’s are the values of the predictor variab… 9.6% of mammograms detect breast cancer when it’s not there (and therefore 90.4% correctly return a negative result).Put in a table, the probabilities look like this:How do we read it? Let’s call him X. Bayesian statistics by example. y_1, ..., y_n | \theta \sim N(\theta, \tau) A mix of both Bayesian and frequentist reasoning is the new era. And they want to know the magnitude of the results. Here’s the twist. The Bayes theorem formulates this concept: Let’s say you want to predict the bias present in a 6 faced die that is not fair. I didn’t think so. real prior information) are used. Bayesian Statistics is a fascinating field and today the centerpiece of many statistical applications in data science and machine learning. maximum likelihood) gives us an estimate of θ ^ = y ¯. I bet you would say Niki Lauda. 42 (237): 72. "puede hacer con nosotros" / "puede nos hacer". A choice of priors for this Normal data model is another Normal distribution for θ. It’s impractical, to say the least.A more realistic plan is to settle with an estimate of the real difference. Frequentist statistics tries to eliminate uncertainty by providing estimates and confidence intervals. 499. Ruggles, R.; Brodie, H. (1947). 3. $OR(+|test+)$ is the odd ratio of having a urine infection knowing that the test is positive, and $OR(+)$ the prior odd ratio. Bayesian Statistics The Fun Way. y_1, ..., y_n | \theta \sim N(\theta, \sigma^2) Simple real world examples for teaching Bayesian statistics? It calculates the degree of belief in a certain event and gives a probability of the occurrence of some statistical problem. Why isn't bayesian statistics more popular for statistical process control? The usefulness of this Bayesian methodology comes from the fact that you obtain a distribution of $\theta | y$ rather than just an estimate since $\theta$ is viewed as a random variable rather than a fixed (unknown) value. We conduct a series of coin flips and record our observations i.e. I think estimating production or population size from serial numbers is interesting if traditional explanatory example. Lactic fermentation related question: Is there a relationship between pH, salinity, fermentation magic, and heat? f(y_i | \theta, \tau) = \sqrt(\frac{\tau}{2 \pi}) \times exp\left( -\tau (y_i - \theta)^2 / 2 \right) The article gives that $LR(+) = 12.2$, and $LR(-) = 0.29$. Bayesian Statistics: Background In the frequency interpretation of probability, the probability of an event is limiting proportion of times the event occurs in an infinite sequence of independent repetitions of the experiment. I accidentally added a character, and then forgot to write them in for the rest of the series, Building algebraic geometry without prime ideals. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. Why does the Gemara use gamma to compare shapes and not reish or chaf sofit? Your first idea is to simply measure it directly. For example, you can calculate the probability that between 30% and 40% of the New Zealand population prefers coffee to tea. P-values and hypothesis tests don’t actually tell you those things!”. P(A) – the probability of event A 4. P (seeing person X | personal experience, social media post) = 0.85. Bayesian statistics deals exclusively with probabilities, so you can do things like cost-benefit studies and use the rules of probability to answer the specific questions you are asking – you can even use it to determine the optimum decision to take in the face of the uncertainties. Höhle, Michael, and Leonhard Held. Use of regressionBF to compare probabilities across regression models Many thanks for your time. To begin, a map is divided into squares. Explain the introduction to Bayesian Statistics And Bayes Theorem? So my P(A = ice cream sale) = 30/100 = 0.3, prior to me knowing anything about the weather. I haven't seen this example anywhere else, but please let me know if similar things have previously appeared "out there". Bayesian statistics is a theory in the field of statistics based on the Bayesian interpretation of probability where probability expresses a degree of belief in an event. Here the test is good to detect the infection, but not that good to discard the infection. Preface. From the menus choose: Analyze > Bayesian Statistics > One Sample Normal Tigers in the jungle. Why are weakly informative priors a good idea? Bayesian methods may be derived from an axiomatic system, and hence provideageneral, coherentmethodology. Bayesian statistics help us with using past observations/experiences to better reason the likelihood of a future event. One simple example of Bayesian probability in action is rolling a die: Traditional frequency theory dictates that, if you throw the dice six times, you should roll a six once. Even after the MLE is finite, its likely to be incredibly unstable, thus wasting many samples (i.e if $\beta = 1$ but $\hat \beta = 5$, you will pick values of $x$ that would have been optimal if $\beta = 5$, but it's not, resulting in very suboptimal $x$'s). Also, it's totally reasonable to analyze the data that comes in a Frequentist method (or ignoring the prior), but it's very hard to argue against using a Bayesian method to choose the next $x$. Bayesian statistics, Bayes theorem, Frequentist statistics. $$OR(+|test+) = LR(+) \times OR(+) $$ \theta | y \sim N(\frac{b}{b + n\tau} a + \frac{n \tau}{b + n \tau} \bar{y}, \frac{1}{b + n\tau}) The prior distribution is central to Bayesian statistics and yet remains controversial unless there is a physical sampling mechanism to justify a choice of One option is to seek 'objective' prior distributions that can be used in situations where judgemental input is supposed to be minimized, such as in scientific publications. This is how Bayes’ Theorem allows us to incorporate prior information. Of course, there may be variations, but it will average out over time. Stack Exchange network consists of 176 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Nice, these are the sort of applications described in the entertaining book. The degree of belief may be based on prior knowledge about the event, such as the results of previous experiments, or on personal beliefs about the event. f ( y i | θ, τ) = ( τ 2 π) × e x p ( − τ ( y i − θ) 2 / 2) Classical statistics (i.e. It provides interpretable answers, such as “the true parameter Y has a probability of 0.95 of falling in a 95% credible interval.”. It does not tell you how to select a prior. Making statements based on opinion; back them up with references or personal experience. By using our site, you acknowledge that you have read and understand our Cookie Policy, Privacy Policy, and our Terms of Service. (2004). 1% of people have cancer 2. If you receive a positive test, what is your probability of having D? Simple construction model showing the interaction between likelihood functions and informed priors A simple Bayesian inference example using construction. This is the Bayesian approach. This article intends to help understand Bayesian statistics in layman terms and how it is different from other approaches. You want to be convinced that you saw this person. That said, you can now use any Normal-data textbook example to illustrate this. The Bayesian paradigm, unlike the frequentist approach, allows us to make direct probability statements about our models. r bayesian-methods rstan bayesian bayesian-inference stan brms rstanarm mcmc regression-models likelihood bayesian-data-analysis hamiltonian-monte-carlo bayesian-statistics bayesian-analysis posterior-probability metropolis-hastings gibbs prior posterior-predictive The probability of an event is equal to the long-term frequency of the event occurring when the same process is repeated multiple times. “Bayesian methods better correspond to what non-statisticians expect to see.”, “Customers want to know P (Variation A > Variation B), not P(x > Δe | null hypothesis) ”, “Experimenters want to know that results are right. It provides a natural and principled way of combining prior information with data, within a solid decision theoretical framework. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. I realize Bayesians can use "non-informative" priors too, but I am particularly interested in real examples where informative priors (i.e. The frequentist view of linear regression is probably the one you are familiar with from school: the model assumes that the response variable (y) is a linear combination of weights multiplied by a set of predictor variables (x). Bayes Theorem Bayesian statistics named after Rev. What's wrong with XKCD's Frequentists vs. Bayesians comic? Strategies for teaching the sampling distribution. The catch-22 here is that to choose the optimal $x$'s, you need to know $\beta$. Are there any Pokemon that get smaller when they evolve? There is a nice story in Cressie & Wickle Statistics for Spatio-Temporal Data, Wiley, about the (bayesian) search of the USS Scorpion, a submarine that was lost in 1968. Integrating previous model's parameters as priors for Bayesian modeling of new data. P(A|B) – the probability of event A occurring, given event B has occurred 2. It provides people the tools to update their beliefs in the evidence of new data.” You got that? Bayesian statistics allows one to formally incorporate prior knowledge into an analysis. Bayesian estimation of the size of a population. The dark energy puzzleWhat is a “Bayesian approach” to statistics? "An Empirical Approach to Economic Intelligence in World War II". The goal is to maximize the information learned for a given sample size (alternatively, minimize the sample size required to reach some level of certainty). 开一个生日会 explanation as to why 开 is used here? Bayesian inference is a different perspective from Classical Statistics (Frequentist). As you read through these questions, on the back of your mind, you have already applied some Bayesian statistics to draw some conjecture. The Bayesian One Sample Inference: Normal procedure provides options for making Bayesian inference on one-sample and two-sample paired t-test by characterizing posterior distributions. Use MathJax to format equations. Each square is assigned a prior probability of containing the lost vessel, based on last known position, heading, time missing, currents, etc. Let’s consider an example: Suppose, from 4 basketball matches, John won 3 and Harry won only one. These distributions are combined to prioritize map squares that have the highest likelihood of producing a positive result - it's not necessarily the most likely place for the ship to be, but the most likely place of actually finding the ship. Many of us were trained using a frequentist approach to statistics where parameters are treated as fixed but unknown quantities. Perhaps the most famous example is estimating the production rate of German tanks during the second World War from tank serial number bands and manufacturer codes done in the frequentist setting by (Ruggles and Brodie, 1947). How can dd over ssh report read speeds exceeding the network bandwidth? Are you aware of any simple real world examples such as estimating a population mean, proportion, regression, etc where researchers formally incorporate prior information? Discussion paper//Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München, 2006. In the logistic regression setting, a researcher is trying to estimate a coefficient and is actively collecting data, sometimes one data point at a time. No. Kurt, W. (2019). 2. The comparison between a t-test and the Bayes Factor t-test 2. Asking for help, clarification, or responding to other answers. 1% of women have breast cancer (and therefore 99% do not). All inferences logically follow from Bayes’ theorem. “Question closed” notifications experiment results and graduation, MAINTENANCE WARNING: Possible downtime early morning Dec 2, 4, and 9 UTC…. When you have normal data, you can use a normal prior to obtain a normal posterior. The example could be this one: the validity of the urine dipslide under daily practice conditions (Family Practice 2003;20:410-2). Depending on your choice of prior then the maximum likelihood and Bayesian estimates will differ in a pretty transparent way. Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. You find 3 other outlets in the city. It often comes with a high computational cost, especially in models with a large number of parameters. Are both forms correct in Spanish? This can be an iterative process, whereby a prior belief is replaced by a posterior belief based on additional data, after which the posterior belief becomes a new prior belief to be refined based on even more data. Think Bayes: Bayesian Statistics in Python. " The Bayesian analysis is to start with a prior, find the $x$ that is most informative about $\beta$ given the current knowledge, repeat until the convergence. The likelyhood ratio of the positive result is: $$LR(+) = \frac{test+|H+}{test+|H-} = \frac{Sensibility}{1-specificity} $$ P (seeing person X | personal experience) = 0.004. Another way is to look at the surface of the die to understand how the probability could be distributed. I was thinking of this question lately, and I think I have an example where bayesian make sense, with the use a prior probability: the likelyhood ratio of a clinical test. We can estimate these parameters using samples from a population, but different samples give us different estimates. Life is full of uncertainties. Why does Palpatine believe protection will be disruptive for Padmé? Bayesian inferences require skills to translate subjective prior beliefs into a mathematically formulated prior. The posterior distribution we obtain from this Normal-Normal (after a lot of algebra) data model is another Normal distribution. One way to do this would be to toss the die n times and find the probability of each face. Why is training regarding the loss of RAIM given so much more emphasis than training regarding the loss of SBAS? site design / logo © 2020 Stack Exchange Inc; user contributions licensed under cc by-sa. To learn more, see our tips on writing great answers. I would like to find some "real world examples" for teaching Bayesian statistics. Bayesian data analysis (2nd ed., Texts in statistical science). the number of the heads (or tails) observed for a certain number of coin flips. The researcher has the ability to choose the input values of $x$. Which game is this six-sided die with two sets of runic-looking plus, minus and empty sides from? The Bayesian method just does so in a much more efficient and logically justified manner. The term “Bayesian” comes from the prevalent usage of Bayes’ theorem, which was named after the Reverend Thomas Bayes, an 18th-century Presbyterian minister. How is the Q and Q' determined the first time in JK flip flop? In order to illustrate what the two approaches mean, let’s begin with the main definitions of probability. Given that this is a problem that starts with no data and requires information about $\beta$ to choose $x$, I think it's undeniable that the Bayesian method is necessary; even the Frequentist methods instruct one to use prior information. It can produce results that are heavily influenced by the priors. Here you are trying the maximum of a discrete uniform distribution. No Starch Press. In a Bayesian perspective, we append maximum likelihood with prior information. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. An Introduction to Empirical Bayes Data Analysis. Is it ok for me to ask a co-worker about their surgery? “Bayesian statistics is a mathematical procedure that applies probabilities to statistical problems.
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