The approach and interpretation of probability associated with Bayes theorem; usually used as opposed to the frequentist approach. It can be seen as an extension of logic that enables reasoning with propositions whose truth or falsity is uncertain. A Bayesian probabilist starts with some prior ...

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Missing approximation to get the Maximum A Posteriori (MAP) estimator of event times with a sparse prior

Assume that a signal $ y $ is a noisy perturbation of time-shifted copies of a given waveform $ f(t) $ defined on K time bins $ \{ 0, \cdots, K-1 \} $: \begin{equation} \forall t \in \{1, \cdots, ...
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1answer
10 views

Maximum a Posteriori (MAP) Estimator of Time Shifts with Poisson Process Prior

Assume that a signal $ y $ is a noisy superposition of time-shifted copies of a given waveform $ f(t) $ on a finite time interval $ [0, T] $: \begin{equation} y(t) = \sum_{i=1}^{n} f(t - \tau_j) + ...
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1answer
38 views

How can we prove this equation using marginalization and conditioning? [on hold]

I want to prove $$P(A|C) = \sum_{B} P(AB|C) $$ How can we prove this using marginalization and conditioning?
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1answer
23 views

Did I draw this tree diagram correctly?

On the way home from work Chris goes through a traffic light then passes over a level crossing, the probability that Chris stops at a traffic light is $\frac{2}{3}$ while the probability that he is ...
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24 views

Sprinkler Example, how to compute probabilities?

Can you explain how do you compute probabilities like, P(S=F|C=T) and P(W=F|S=F,R=T) ?
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26 views

Bayesian Estimation Derivation

I am trying to understand Bayesian estimation and I come across this line in my lecture notes: θ(Bayesian) = E_θ|x[θ] = E[π(θ|x)] So it's meant to reader that ...
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11 views
+50

Does the initial prior affecct the asymptotic performance of Kalman filter, why?

More specifically, can the observed data can gradually correct the incorrect initial prior? Thank you very much!
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1answer
29 views

Calculate Conditional Probability for program that does not crash

I got these 2 questions in exam, but unfortunately i failed to solved these. 1) you want to buy a computer. The probability that you can run the probabilistic program $X$ on it is $97$% ...
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10 views

Find the 1 dimensional empirical conditional distribution with data

I need to find the the conditional distribution function empirically of some data $C$, given the value of a particular predictor $y$. I have attempted to use Bayes as follows: $$ P(C \ |\ ...
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1answer
19 views

Simple Bayes? Probability of a state at time t in hidden markov model

Suppose we have a HMM with $2$ states -- $A$ and $B$, with $P(A) = 0.4$ and $P(B) = 0.6$. $A$ has a probability of $0.9$ of outputting "hot," and $B$ has a probability of $0.1$ of outputting "hot." ...
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1answer
22 views

Application of Bayes theorem and Partition Law, total probability

Hi guys, preparing for my finals and trying to get this question out for practice. The exam is in a couple of hours so apologies for being brief. I think I have computed parts 1 and 2 fine. ...
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12 views

Getting Started in Bayesian Statistics [duplicate]

I want to start learning about Bayesian statistics. What resources would you recommend? If possible, I think for future readers it would be helpful if answers could be broken up into (1) overview ...
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12 views

OpenBUGS: get a sample from a random variable

I'm working in OpenBugs, and I've defined the next model: $Y\sim {\rm Exp}(\theta)$ so I'm asked to assign different initial distributions to $\theta$: Normal, Gamma and log-normal, to this point ...
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19 views

Simple example of “Spike-and-Slab Prior” for Bayesian Inference

I would really like to understand how Spike-and-Slab Priors work in relation to Linearized Models. Can somebody provide a toy example of a Spike-and-Slab Prior with a Bernoulli spike and a Gaussian ...
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0answers
15 views

How many people should I ask if a statement (A) is true if the same can be inferred by asking two other statements (X and Y implies A)?

I am asking a number of participants if they believe a given statement is valid. I have a number of such statements, some of which can be inferred. In the made up example below, ...
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1answer
55 views

Approximation of product of Bernoulli with different proportions

I want to update a variable $Y$ with Beta (uniform for simplicity, $Y \sim U(0, 1)$) distribution, with Bernoulli information each period... But each period the proportion parameter of the Bernoulli ...
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0answers
6 views

The Intergration of the Product of Two Conditional Probabilitie

I have been stragling with figuring out this equality: $\pi(\psi|data)=\int_{\Theta}\pi(\psi|\theta)\pi(\theta|data)d\theta$ Can anyone help me go through the proof? Thanks!
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1answer
13 views

Iterated Bayesian Updates

I get a sequence of data that is generated by a distribution with parameter $a_0$ (e.g. $\mathcal{N}(a_0,1)$). I assume a prior distribution $P(a)$, and Bayesian update for the belief according to the ...
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0answers
16 views

Non linear model, logistic regression exercise

Let $y_i$ follows $Bin(n_i,p_i)$ and for $p_i$ we consider the logit quadratic model: $\log\frac{p_i}{1-p_i}=\beta_0+\beta_1A_i+\beta_2(A_i-meanA)^2$ where $A_i$ is AGE_i during ith time. As you can ...
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2answers
14 views

Show that $\frac{\alpha+y}{\alpha+n+\beta}\in (\frac{\alpha}{\alpha+\beta};\frac{y}{n})$

Suppose you assign a $Beta(\alpha,\beta)$ prior distribution for $\theta$, and the you observed $y$ heads out of $n$ spins. Show algebraically that your posterior mean of $\theta$ always lies ...
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1answer
45 views

Bayesian probability

recently I bumped into following puzzle and I would like to validate(or correct) my results as I asked several people and got several different answers. You are planning a picnic with your friends ...
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76 views

Probability of number of people who know a rumor

Suppose that among a group of $n$ people, some unknown number of people $K$ know a rumor. If someone knows the rumor, there is a probability $p$ that they will tell it to us if we ask. If they don't ...
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10 views

Expectation Maximization (EM) for 3-dimensional parameter $(\alpha,\mu_2,{\sigma}^2)$.

Let $x_i$ where $i=1,...,100$ are iid observations from a mix of two normal distributions with means $\mu_1=0$ and $\mu_2$ and the same variance ${\sigma}^2$. If $\alpha$ is the proportion of the ...
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14 views

Hypothesis test in Bayesian statistics

Let $X\sim N(\theta,1)$ and 5 independent observations $X=(4.9,5.6,5.1,4.6,3.6)$. The prior probability that $\theta=4.01$ is $0.5$. The remain values of $\theta$ are given the density of ...
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1answer
37 views

How does the posterior of a dirac prior look like?

Edit for the Moderators: Should this question migrate to stats.stackexchange? I have a very basic question concerning updating from a prior to a posterior in bayesian statistics. Setting: I ...
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2answers
65 views

Bayesian urn questions

There are two urns, each with four ping-pong balls. In one urn, three of the balls are red, and one is white; in the other, three are white, and one is red. Without knowing which urn you are choosing, ...
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0answers
42 views

Facebook Data Science Question (Expected Payout and Probability)

I saw this question on Glassdoor and couldn't seem to find a answer to validate mine anywhere: You're at a casino with two dice, if you roll a 5 you win, and get paid $10. What is your expected ...
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0answers
20 views

Posterior of Normal with prior Cauchy

Let $X\sim N(\theta,1)$ and $\pi(\theta)\sim \mathrm{Cauchy}(0,1)$ find a 90% credible set for $\theta$ To find the credible set I need to find the distribution of $f(\theta\mid x)$, but ...
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1answer
30 views

Bayes' Rule Question

I am reading about Bayes' rule, I can solve all the exercise but this one. Suppose you had a checkup, and there is a bad news; you tested positive for "the giggles" and that the test is 99% accurate( ...
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1answer
26 views

how to calculate conditional independence

This Bayesian net (click) is given with the binary variables B, F, G and D and the following probabilities $p(B=1) = 0.9$ $p(F=1) = 0.9$ $p(G=1\mid B=1,F=1) =0.8$ $p(G=1\mid B=1,F=0) = 0.2$ ...
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12 views

Bayes Rule and Multivariate Normal Estimation

This is an exercise in this pdf file http://statweb.stanford.edu/~ckirby/brad/LSI/chapter1.pdf and how can I show that by using Bayes Rule?
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1answer
27 views

Book recommendations for introductory Bayesian statistics?

Anyone here have some recommendations for a good book introducing the reader to Bayesian statistics? Let me mention my background. My undergraduate majors were in Actuarial Science and Statistics, ...
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1answer
30 views

What does the notation $d|x \sim N(0,14^2)$ stand for?

I'm reading a book about Bayesian data analysis (by Gelman et al.) and I bumped into the following text: $x= \text{Football point spread}$ $y=\text{Game outcome}$ $d=y-x$ For the ...
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0answers
19 views

Find pmf for binomial distribution with prior

Let $X$~$Bin(n,P)$ where $P$~$Beta(\alpha,\beta)$. How do I find the pmf for $X$? I have a vague idea that I have to condition on $P\leq \tilde{p}$ to find $Pr(X=x|P\leq\tilde{p})$ but I'm not ...
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0answers
11 views

Marginal probabilities

I am stuck on the following problem of calculating marginal probabilities, which I have highlighted in yellow: Given the information below, how do we calculate$ p(X=0|w=\frac{1}{4}), ...
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0answers
9 views

Bayes risk and Bayes decision

We are considering a sample of size $n$ from an exponential distribution, with parameter $w >0$. We wish to produce an estimate for $d$, for $w$ , with loss function: $L(w, d)=w(w-d)^2$ The prior ...
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0answers
19 views

Ranking players and puzzles from performance in a single player game format

I have a 1000 crossword puzzles and a 1000 solvers - each individual is assigned a 100 arbitrary puzzles to solve (so each solver gets exactly 100 puzzles but each puzzle could have 1-1000 solvers) - ...
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1answer
38 views

Integrating a Delta Function of a Sum

As part of an inference project, I'm normalising a prior distribution which vanishes unless the set of $M$ data points $f_1,...f_M$ satisfies $$ \sum_{i=1}^M f_i = 1. $$ Accordingly this is encoded ...
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1answer
21 views

How does one compute this bayesian probability?

Assume you have a network as follow (where X->Y implies X is the parent of Y) A->D, B->D How does one compute $P(A,B|D)$? A and B are independent so my intuition tells me $P(A,B|D)= ...
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1answer
24 views

Probability of receiving a bit correctly.

Bits are sent through an information channel, the probability of incorrectly receiving a $1$ is 0.02, while the probability of incorrectly receiving a 0 is 0.01. What is the probability of receiving a ...
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1answer
70 views

Let. $X \sim \mathcal{U}(0,1)$. Given $X = x$, let $Y \sim \mathcal{U}(0,x)$. How can I calculate $\mathbb{E}(X|Y = y)$?

Suppose that $X$ is uniformly distributed over $[0,1]$. Now choose $X = x$ and let $Y$ be uniformly distributed over $[0,x]$. Is it possible for us to calculate the "expected value of $X$ given $Y = ...
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0answers
19 views

Gaussian distribution with Gamma variance

I am using a hierarchical Bayesian model. In one part of it, I have a normal distribution with mean zero and a variance sampled from a Gamma distribution for some hyper-parameters $a_0$ and $a_1$: ...
1
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1answer
35 views

Posterior Predictive Distribution for a coin toss

In this question, i can work out that the posterior is supposed to be a Beta (r+1, n-r+1) distribution. However, what I am struggling with is how to compute f(X_n+1|theta). Is this the binomial ...
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12 views

Horseshoe estimator posterior

Suppose given the Horseshoe estimator: $Y|\beta,\sigma^2 \sim N(X\beta,\sigma^2 I)$ $\beta|\sigma^2,\tau_{1}^2,...,\tau_{p}^2 \sim N(0,\sigma^2 D)$ $\tau_{j} \sim C+(0,1)$ $\sigma^2 \sim \pi ...
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12 views

Dirichlet process mixture model

I'm reading Nonparametric Bayesian Inference by Peter Müller and Abel Rodriguez. In Chapter 3, there is no proof provided for some formulas but I think I need to know exactly how it was derived if I ...
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18 views

Bayes–If the prior is increased by a factor of n, what happens to the posterior? If using a threshold, does higher prior mean more “false positives”?

If we're using Bayesian inference in two situations where everything is the same, except that the prior in one is n times the prior in the other, is there anything we can say about how the posteriors ...
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1answer
24 views

Using head-to-head results and Bayes' Theorem to modify predictions of sport/game contests that are initially derived from Elo-type ratings

I am working on an extension of the Glicko2 rating system to use in predicting the outcome of sport/game contests that uses the actual head-to-head results of previous meetings of competitors to ...
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0answers
12 views

How to determine the transition probability in Sequential Importance Sampling (SIS) for Particle Filter

Given a state-space model \begin{align} x_k &= f_k(x_{k-1}, v_{k-1}),\\ z_k &= h_k(x_k, w_k), \end{align} where $x_k \in {\mathbb R}^{n}$ and $y_k \in {\mathbb R}^{m}$ are the system state ...
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18 views

Drawing uniform samples from the *range* of a non-invertible function

I am looking for a Bayesian technique to draw samples from a uniform distribution over the range of a non-invertible (that is, there isn’t even a formula) function $\mathbf{f}: \mathbb{R}^N ...
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29 views

Vector Euclidean norm upper bound by his coordinates average.

I'm trying to extend the Rademacher complexity and have the following question: For $ (v_1,..,v_m) = V \in {\mathbb{R}}^{m} $ , I will be glad to find an upper abound for the Euclidean norm: $$ ...