Bayesian inference is a method of statistical inference which uses Bayes' theorem to find probability estimates of parameters or hypotheses.

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Is there an article/book reviewing different methods for constructing posterior point/interval estimates?

Given a one-dimensional posterior distribution it is often the case that you want to calculate a point estimate and a credible interval for the corresponding parameter. There are, of course, many ways ...
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Selecting priors based on measurement error

How do you calculate the appropriate prior if you have the measurement error of an instrument? This paragraph is from Cressie's book "Statistics for Spatio-Temporal Data": It is often the case ...
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posterior distribution if I dont Know likelihood [on hold]

How can I find posterior if I know prior is Gamma(a,b) and likelihood is augmented ~~~~~~~~~~~~~~~~~~~~~~~~~~~
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17 views

Using interaction terms in an MCMCglmm

I am using MCMCglmm models in R, with hierarchically nested data. The basic structure of the data is as follows - each dyad is a unique combination of focal/other: ...
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17 views

How do I define the number of parameters needed to specify this Bayesian network?

If it is known that Bayesian network has one root node, one node with a single parent, two nodes with two parents and the remaining nodes with 3 parents, indicate how many parameters would be required ...
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77 views

Implementation of hierarchical bayesian model produces errors with PyMC

I'm trying to solve an exercise from this book in which I'm supposed to fit data on temperature and elevation in Colorado to this model: \begin{equation} \boldsymbol{Y} = \boldsymbol{\mu} + ...
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Rstan model for a simple mixture of normals

This blog post from Rbloggers describes how to code a simple three-part normal mixture model with known mixing coefficients, means and standard deviations. While it describes the procedures in some ...
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31 views

R Bayesian prediction of a Gaussian process

I have a Gaussian model with mean zero, variance is arbitrary constant, and correlation function $e^{-\theta(x-x')^2}$ where $\theta$ is again an arbitrary constant. I've plotted some realizations of ...
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92 views

How to use Bayesian statistics to estimate mean and a covariance matrix given structured observations

I have a real-world problem in which the observations are linear combinations of elements in a vector $\bf{c}$. However, there exist correlation between different element in $\bf{c}$. For example ...
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32 views

Bayesian models vs Bayesian network models

I'm new to statistical modeling and working on applications in spatial property prediction. Can you help me understand the difference between a hierarchical bayesian model and a bayesian network ...
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37 views

Informative prior for a binomial proportion close to one (or zero)

I want to do inference on a binomial proportion, which I have reason to believe a priori is close to one. Let's say my prior expectation is 0.98. I'd like to do incremental updating of my beliefs as ...
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22 views

Posterior autocorrelation in Pymc. How to interpret it?

I started learning Bayesian inference by reading "Probabilistic Programming and Bayesian Methods for Hackers". I found something that is not really clear for me in the third chapter. Lets look at the ...
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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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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 ...
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23 views

Estimate time from interval-censored data with bayesian approach

I have interval censored data for incubation period and I suppose that the exact time Ti is included in each interval of the data [L;U] and follows a lognormal distribution (mu,sigma2). I would like ...
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Why “explaining away” makes the inference over posterior hard? [closed]

Why "explaining away" makes the inference over posterior hard? At least the is what I understand from the 1st paragraph of Section 2 at [1]. I found a few relevant discussions about this on the ...
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25 views

Inference methods for multimodality and label switching

Imagine that there are three professions in the world $a,b,c$ (astronauts, doctors and statisticians) and that the Gross Domestic Product (GDP) of a city can be modeled as a linear regression of its ...
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Bayesian inference MCMC model for interval censored data

I have interval censored data for incubation period and I suppose that the exact time Ti included in each interval of the data [L;U] follows a lognormal distribution (mu,sigma2). I would like to use a ...
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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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tutorial on sampling methods and MC

I'm looking for good tutorials that cover the various sampling methods: simple sampling, MCMC, Gibbs Sampling, and Metropolis Hastings Algorithm. I barely know what is an MCMC. I would like to learn ...
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Bayesian modeling using multivariate normal with covariate

Suppose you have an explanatory variable ${\bf{X}} = \left(X(s_{1}),\ldots,X(s_{n})\right)$ where $s$ represents a given coordinate. You also have a response variable ${\bf{Y}} = ...
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Is my Bayesian analysis correct?

This is my first time doing a Bayesian analysis, so I'm not sure whether what I did makes perfect sense. I'm trying to tell if two samples come from the same distribution, more specifically, if they ...
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Are pooled results from multiple imputation equivalent to a posterior mean?

I am fairly new to multiple imputation and trying to be sure I understand the approach. Say I have a data set with missing values, so I create 5 imputed data sets using multiple imputation by ...
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Bayesian Updating Process, 3 signals and 2 states of the world

Suppose Nature chooses a state $\omega = \{X,Y\}$ at $t=0$. Long-lived agents observe a signal $s_t$ at every period $t$, where $s_t = \{ x,y,z \}$. Agents all hold a common prior $\mu_0 \in (0,1)$ ...
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28 views

MCMC-Draws from different MCMC- chains

I have questions about the usage of draws from a MCMC. I estimate a hierarchical bayesian Multinomial Logit model (using bayesm in R). I am interested in the ratio of two coefficients 1 and 2, say ...
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java.lang.OutOfMemoryError using bartMachine package in R [migrated]

I ran a BART model with 11000 samples and 20 features(half of them are categorical variable). My mac has 8G ram. At first, I set memory to 5000 MB via function set_bart_machine_memory(5000). Then I ...
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Sequential weighted sampling

I need to figure out the total path (A to Z) followed by an agent through a squared-element grid. Each grid element has a probability density function $\Delta$ assigned to it that represents the ...
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P values depend on researchers' sampling intentions

John Kruschke has written widely on the misleading information provided by p values. I understand nearly everything he says, although there is one aspect I am a little confused about. Kruschke ...
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Posterior parameter distribution

I am considering the following non-linear state space model: $X_t=\frac{X_{t-1}}{2}+25\frac{X_{t-1}}{1+X_{t-1}^2}+8\cos{1.2t}+\epsilon_t, \epsilon_t\sim N(0,\sigma_x^2 ) $ ...
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74 views

Predictive posterior distribution with multivariate normal distribution

Suppose I have a multivariate normal ${\bf{Y}}|{\bf{\theta}} \sim {\bf{MVN}}(X {\bf{\beta}}, \sigma^{2}H(\phi))$ where ${\bf{Y}}$ is a set of observations ${\bf{Y}} = ...
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1answer
37 views

Simulating from a normal with “unknown” variance

Suppose I want to performing sampling from a normal distribution with an unspecified variance, and I want a way to sample so that I am in some sense "averaging out the possible values of the ...
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15 views

Posterior Predictive Checks

I understand what the posterior predictive distribution is, and I have been reading about posterior predictive checks, although it isn't clear to me what it does yet. What exactly is the posterior ...
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25 views

Empirical Bayes vs “non-informative” priors

I am familiar with the mechanics with both methods, but don't know what factors I should consider when choosing between these two approaches for adjusting a prior. I would imagine that, on a case by ...
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How does one interpret the distribution over parameters in bayesian estimation?

I am new to Bayesian estimation. The assumption that the parameters are random variables seems a little unsettling to me. For example when considering a model for data, what physical interpretation ...
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What is the mathematical difference between using a un-informative prior and a frequentist approach?

Un-informative priors are preferred in instances where bias is not acceptable (ie. courtrooms, etc.) However, it seems to me that it would just make sense to use a frequentist approach instead. Why ...
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162 views

MCMC packages in R

Is there an R package for MCMC that can accept my self-defined (log)likelihood function (can be done in MCMCpack) and lets the user define contraints to the ...
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PYMC Confusion: are observed nodes fixed or stochastic?

I've been trying to gain a better understanding of factor potentials in PYMC. In reading this article by Cam Davidson-Pilon on Yhat, I got confused about how observed nodes are understood by PYMC. ...
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Bayesian Spatial Prediction: What is f?

I'm working my way through Gelfand, A.E. & Li Zhu, B.P.C. (2001). On the change of support problem in spatio-temporal data. Biostatistics, 2:1, 31-45. I'm stuck at: This is absolutely the ...
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136 views

Deriving the posterior density for a lognormal likelihood and Jeffreys's prior

The likelihood function of a lognormal distribution is: $f(x; \mu, \sigma) \propto \prod_{i_1}^n \frac{1}{\sigma x_i} \exp \left ( - \frac{(\ln{x_i} - \mu)^2}{2 \sigma^2} \right ) $ and Jeffreys's ...
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Is rstan or my grid approximation incorrect: deciding between conflicting quantile estimates in Bayesian inference

I have a model to achieve Bayesian estimates the population size $N$ and probability of detection $\theta$ in a binomial distribution solely based on the observed number of observed objects $y$: $$ ...
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Bayesian inference of a clinical trial for clinicians

I am a clinician who is more adept than average at interpreting clinical trials in a frequentist manner. At this point, interpreting a trial as a frequentist has kind of become a procedure: check ...
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2answers
76 views

Inferring prior distribution

Suppose that we take a sample ($X_1, X_2, ... X_n$) from a distribution where we assume that $X_i $~$ Bin(n_i, p_i)$ and $n_i$ is known for every $i$. We also assume that $p_i$'s are independent and ...
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E-mail answered probability after n days of waiting for a reply - based on a sample of e-mails and replies

Here is the task: I have a sample of replies to my e-mail from my mail box. A sample is taken over a period of 90 days, 1000 e-mails and replies if any. (We only consider a pair of {my original ...
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multiple imputation for categorical data question

How do i design the use of Multiple Imputation based on Bayesian Inference when I am dealing with categorical data and my dataset does not contain complete prior observations at every combination ? ...
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152 views

MCMC of a mixture and the label switching problem

I generated some data according to a mixture of two lognormals: $f(x) = p \cdot \mathcal LN(\mu_1, \sigma) + (1-p) \cdot \mathcal LN (\mu_2, \sigma)$. given $p$ and $\sigma$, this is my code to find ...
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Bayesian estimation of $N$ of a binomial distribution

This question is a technical follow-up of this question. I have trouble understanding and replicating the model presented in Raftery (1988): Inference for the binomial $N$ parameter: a hierarchical ...
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Estimating $n$ and $p$ for Binomial distribution, repeated counting of partly hidden population

A brief motivation: $n$ critters live in an aquarium, where sadly they often hide in, under or behind things. When the aquarium is observed, each critter is only seen with probability $p$ ...
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what to chose for prior and proposal function for MCMC of a mixture

I generated some data according to a mixture of two lognormals: $f(x) = p \cdot \mathcal LN(\mu_1, \sigma) + (1-p) \cdot \mathcal LN (\mu_2, \sigma)$ Now I want to use MCMC to find the parameters $p, ...
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How can we combine learnings from multiple experiments in a single causal model?

I would like to use a causal network modelling to model the interaction of several variables and the effects of interventions. I have measurements for all priors of the model, that is without any ...
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Posterior distribution of proportional difference of two binomial variables

Can somebody point me in the right direction for a treatment of the following problem? I imagine this should be a fairly common problem in medical statistics... Given two binomial random variables ...