# Questions tagged [bayesian]

Bayesian inference is a method of statistical inference that relies on treating the model parameters as random variables and applying Bayes' theorem to deduce subjective probability statements about the parameters or hypotheses, conditional on the observed dataset.

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### Help me understand the Bayesian kernel density estimation (Sibisi and Skilling, 1996)

Sibisi and Skilling (1996, also mentioned in the 1997 paper) define Bayesian kernel density as $$f(x) = \int dx' \,\phi(x')\, K(x, x') \tag{2}$$ Here the kernel $K$ is an assigned smooth ...
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### Official name of a common type of Bayesian simulation study

There is a kind of simulation study that is commonly used to validate an implementation of a Bayesian model: For independent replication $i = 1, ..., n$: Draw a set of "true" parameters ...
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### Hypergeometric: how do I construct a credibility interval around K (population successes) in R?

I have a problem for which I believe I should use the hypergeometric distribution, but I can't figure out how to do it in R. Say I have a bag of marbles with known number ($N$) of marbles, but the ...
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### How to guess the size of a set?

Assume we have a set of unique words and draw a number $n$ of them using simple-random-sampling without replacement independently in each round. We have several rounds and try to guess the set size ...
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### Pope effect on pizza - Regression with presence absence and similarity data as dependent variables

I'm trying to figure out the right way to set up a regression when the dependent variables are presence absence data (of pizzas), and the similarity between the present pizzas. Bear with the story: ...
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### Fourier transform of a Gaussian process

I would like to discuss and ask a question regarding the Fourier transform of a Gaussian process, if it makes sense. For that purpose, let me describe the following situation. Let $z(s)$ be a ...
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### Rationale behind Good–Turing frequency estimation?

Good–Turing frequency estimation is a smoothing estimator for estimating a multinomial distribution. It seems very convoluted. From mathematical statistics point of view, what is the rationale ...
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### PyMC3 implementation of Bayesian MMM: poor posterior inference

Google released a whitepaper on Media Mix Modelling (MMM) in 2017; vanilla MMM (established in the 1960s) uses multivariate regression. It's a decent mechanism to understand which of your marketing ...
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### Singular fit with simplest random structure in lmer (lme4), is a Bayesian approach the only option?

I'm running a mixed model with the lmer function from the lme4 package in R and ran into some issues with singular fits. I get the warning message 'singular fit', ...
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### Implementing Predictive Posterior Distribution Using Stan

Background I had an example that sought to demonstrate the posterior predictive distribution in the context of a normal measurement model. The data that was used is as follows: ...
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### Mean, median, or mode of skewed posterior?

I'm estimating an ICC from 2 and 3-level hierarchical models using rstanarm. The simplest models are: y ~ (1|group) or ...
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### Density estimation/approximation from MCMC samples

I'm looking to accurately describe the density function of a multivariate posterior probability distribution based on samples from MCMC. As far as I know, in most cases this is done either with a ...
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### Generalization of degrees of freedom for t distribution for coefficients after multiple imputation

Donald Rubin has shown that regression coefficient estimates have fatter tails after multiple imputation and has provided a formula for the degrees of freedom to use as a t-distribution approximation ...
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### Horseshoe priors and random slope/intercept regressions

I'm interested in using the horseshoe prior (or the related hierarchical-shrinkage family of priors) for regression coefficients of a traditional multilevel regression (e.g., random slopes/intercepts)....
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### Priors as Controls : Bayesian Regression

I have a general question about Bayesian Regression Modeling and how a prior might be used as a means to control for (close to) simultaneous events. I often face a situation where I have a time series ...
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### Why does Quadratic (Normal/Laplace) Approximation fail on multilevel models?

In Statistical Rethinking, 2nd Edition, section 13.1, Richard McElreath says: Why doesn’t simple quadratic approximation, using for example quap, work with multilevel models? When a prior is itself a ...
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### Trouble replicating simple example of Bayesian inference

On pages 20-21 of John Kruschke's Doing Bayesian Data Analysis book (2nd ed.), there is an introductory illustration of Bayesian inference. We know that balls can have four sizes: 1, 2, 3 and 4, but ...
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### Are Log Predictive Likelihood, Log Predictive Probability, Log Marginal Likelihood and Log Predictive Density same?

I have seen different papers use different terms to express the scoring rules that they used to compare Bayesian models. Some of those terms are, Log Predictive Density (Bayesian Data Analysis - by ...
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### Bayesian inference on mean of statistic from population

Suppose that a collection of time intervals $t_i$ have occurred, for $i=1,...,n$. These should be considered as samples from a population governed by some distribution. During these time intervals, ...
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### Time evolution of a Bayesian posterior

I have a question regarding the time evolution of a quantity related to a Bayesian posterior. Suppose we have binary parameter space $\{ s_1, s_2 \}$ with prior $(p, 1-p)$, The data generating ...
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### Dealing with dependent data in a Bayesian model

Background: Consider a series of dependent data points, $$y_1,y_2,y_3,\cdots,y_N.$$ In cases where the dependence is well described by an exponentially decaying auto-correlation function, it is ...
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### Cox's Theorem: ignorance, objective priors, and the Mind Projection Fallacy

I've been trying to understand Cox's Theorem and the problems surrounding it. There's so much information on this topic that I've become confused as to the exact state of the theorem. I've gathered ...
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### Is my OpenBUGS / WinBUGS model well specified?

I've just started trying to use OpenBUGS for Bayesian analysis of stochastic volatility models. In particular, I'm trying to calculate stochastic covariance, similar to the DC-MSV model specified by ...
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### Question 10.9 from Bayesian Data Analysis, what does accuracy mean here?

I'm doing an independent study in Bayesian Statistics following some chapters from BDA3. When solving the first question from Ch 10 I got stuck. It says: [If] a scalar variable $\theta$ is ...
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### Unscented Kalman filter-negative covariance matrix

I have recently started working on the unscented Kalman filter. I coded the numerically stable version (i.e., square root Kalman filter) and used MATLAB for implementing. In the final update step, ...
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### Generate Posterior predictive distribution at every step in the MCMC chain for a hierarchical regression model

I'm trying to fit a Bayesian Hierarchical regression model with a random correlated coefficients using R ,I'm using data having 160 groups (schools) to fit a model of math score as a function of one ...
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### Adding a magnitude penalty to a GAM

This is a follow-up to a previous question of mine, explaining the problem in more detail in the hopes of getting more precise advice. Consider the following structured additive regression model or ...
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Suppose you have i.i.d. variables $x_i$ in ${1,\ldots,K}$ modeled as $$P(x_i = k) = \theta_k$$ and and you want to infer the probability vector $\theta$. A Bayesian approach puts a prior over $\... 7 votes 0 answers 146 views ### Bayesian inference via approximate data likelihood Suppose that we have a very large i.i.d. sample$x_1,...,x_n$and a data likelihood defined by $$p(x | \theta,\beta) = \prod_ip(x_i | \theta,\beta)$$. Further suppose that$\theta$is the parameter ... • 176 7 votes 0 answers 2k views ### Dealing with auxiliary random variables for Mean-Field Variational Inference in Bayesian Poisson factorization I am studying as a part of a class assignment a recent paper on Poisson factorization. Some points of the paper regarding the usage of some auxiliary variables are not clear to me. I would like to ... 7 votes 0 answers 282 views ### Cox's Theorem: the necessity of (un)countably additivity I've been trying to understand Cox's Theorem and the problems surrounding it. There's so much information on this topic that I've become confused as to the exact state of the theorem. I've gathered ... • 551 7 votes 0 answers 3k views ### Help with a proof of Bayes classifier optimality I have a class assignment to provide a proof that Bayes classifier for the two label version is optimal in that it's error rate is always${\le}$any other classifier. I've never worked through a ... • 695 7 votes 0 answers 239 views ### why use diagonal$\Sigma$when working with Bayes decision theory? My prof. said in the class that for Bayes decision rule, the likelihood is Gaussian and in practice, we will almost always work with a diagonal$\Sigma$. Why is that? I know that a diagonal$\Sigma$... • 235 7 votes 1 answer 570 views ### Bayesian estimates for Deming regression coinciding with least-squares estimates Consider the following Deming model with independent replicates :$$x_{i,j} \mid \theta_{i} \sim {\cal N}(\theta_{i}, \gamma_X^2), \quad y_{i,j} \mid \theta_{i} \sim {\cal N}(\alpha+\beta\theta_{i}, \... • 19.6k 7 votes 0 answers 170 views ### Is this problem Bayesian? And can I use variational approximation? Suppose there are$N$samples of observations$\mathbf X(n)$($n=1,\cdots,N$), which are given by probability distribution$p(\mathbf X(n)|\mathbf Z(n))$with their conditions are given by hidden ... 7 votes 0 answers 2k views ### Parameter Estimation for Naive Bayes - Maximum a posteriori and Maximum Likelihood I am wondering if I understand those terms correctly. To summarize my thoughts: In naive Bayes, our decision rule is basically the Maximum a posteriori (MAP) estimate of our hypothesis. We assign an ... 7 votes 3 answers 723 views ### Credit Risk and Concentration I am working with a UK credit-union and we are looking to build a model to assess our credit risk and changes to this over time. We have a number of loans to borrowers who each have a credit rating (... • 71 7 votes 0 answers 1k views ### Bias Variance tradeoff from a Bayesian perspective I know the general question about bias variance has been asked before. I understand the frequentist approach and the concept of model selection and the impact of bias and variance on "accuracy" of a ... 7 votes 0 answers 2k views ### Combining posterior probabilities from multiple classifiers I am new to machine learning and can't get my head around this problem. I have two patient datasets, the first ($D_1$) contains$Y,Z,X$that convey blood-sample information and the second ($D_2$) ... • 111 7 votes 0 answers 6k views ### Conjugate of Weibull with shape known This isn't exactly a homework problem but rather a self-selected problem I'm doing to prepare for a midterm. I can see from Wikipedia that it is an inverse gamma but I am unable to reach the ... • 1,033 6 votes 1 answer 180 views ### No more than$n\$ moose, but how many?

Introduction I am thinking about how to estimate the number of individual moose from wildlife camera photos. I have the latitude and longitude position of each observation, along with a datetime of ...
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