Questions tagged [bayesian-probability]
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89 questions
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Bound on covariance of Gaussian-reweighted probability measure
The problem
Let $\rho$ be a probability measure on $\mathbb R^d$ and define the probability measure $\nu_x$ via
$$
\mathrm d\nu_x(y) \propto \exp(-(y-x)^TK^{-1}(y-x))\mathrm d\rho(y),
$$
where $K$ is ...
- 15
0
votes
1
answer
96
views
Rate of convergence for the mean square error $\mathbb{E}[||\hat{\theta}_n - \theta^* ||^2 ]$ of MAP estimators
I study the statistical properties of the Maximum A Posteriori (MAP) estimator under favorable conditions.
Given $n$ samples, the MAP estimator is defined by
$$
\hat{\theta}_n = \arg\max_{\theta} \...
- 2,602
1
vote
1
answer
120
views
On the MSE of MAP estimators and model mismatch
We consider an estimation problem where the parameter $\theta$ is assigned with the prior $g_\alpha$ depending on some parameter $\alpha$ (e.g. the variance of a Gaussian prior) and the observation $...
- 2,602
4
votes
0
answers
178
views
Is there a structural way to show that Ridge regression is equal to a weighted average of lower dimensional models?
Let $X$ be an $n \times k$ matrix. An interesting result of Leamer and Chamberlain (1976) establishes that the Ridge estimator satisfies the following identity
\begin{equation}\label{eq:1}
\hat{\beta}...
- 35
1
vote
0
answers
98
views
Convergence of iterated average Bayesian posterior to high entropy distribution
Setup
Assume $p_Y \in \Delta^n$ is a probability vector obtained by $p_Y=L_{Y|X}p_X$, where $L_{Y|X} \in \mathbb{R}^{n \times m}$ is an arbitrary likelihood (i.e, a column stochastic matrix) and $p_X \...
- 11
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0
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183
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Which proposal distribution should be used in this particular case of the Metropolis-Hastings algorithm?
As part of my research, I would like to apply the Metropolis-Hastings in order to sample from some posterior distribution. More precisely, the data comes from a multivariate normal distribution in the ...
- 59
1
vote
1
answer
170
views
Bayes classifiers with cost of misclassification
A minimum ECM classifier disciminate the features $\underline{x}$ to belong to class $t$ ($\delta(\underline{x}) = t$) if $\forall j \ne t$:
$$\sum_{k\ne t} c(t|k) f_k(\underline{x})p_k \le \sum_{k\ne ...
- 31
1
vote
0
answers
94
views
Gibbs Priors form a Martingale
I am working on adapting variational inference to the recently developed Martingale posterior distributions. The first case, which reduces the VI framework to Gibbs priors, is proving hard to show as ...
- 29
2
votes
1
answer
380
views
Sum of arrival times of Chinese Restaurant Process (CRP)
Suppose that a random sample $X_1, X_2, \ldots$ is drawn from a continuous spectrum of colors, or species, following a Chinese Restaurant Process distribution with parameter $|\alpha|$ (or ...
- 141
0
votes
1
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175
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Existence and uniqueness of a posterior distribution
I am wondering about the existence and uniqueness of a posterior distribution.
While Bayes' theorem gives the form of the posterior, perhaps there are pathological cases (over some weird probability ...
10
votes
1
answer
423
views
Who introduced the term hyperparameter?
I am trying to find the earliest use of the term hyperparameter. Currently, it is used in machine learning but it must have had earlier uses in statistics or optimization theory. Even the multivolume ...
- 923
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0
answers
89
views
Canonical information geometry for probability distributions on different parameter spaces
I am interested in a canonical information geometry on spaces of probability distributions containing distributions with different parameter spaces. Let me give some context and practical motivation ...
- 203
3
votes
0
answers
106
views
Confusion with implementation of PDE constraint Bayesiain inverse problem
Consider a PDE,
$$\partial_t u -a \nabla u - ru (1-u) = 0$$
at a given snapshot in time. The inverse problem is to find the diffusion coefficient $a \in L^{\infty}$ from a noisy measurement $$Y = \Phi(...
- 81
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0
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102
views
Probability distribution for a Bayesian Update
I am struggling with a process like this:
$$X_t=\begin{cases}
\frac{\alpha\omega_t}{\alpha\omega_t+\beta(1-\omega_t)} & \text{with prob } p\\
\frac{(1-\alpha)\omega_t}{(1-\alpha)\omega_t+(1-\beta)(...
- 21
0
votes
1
answer
138
views
How does this Bayesian updating work $z_i=f+a_i+\epsilon_i$
$z_i=f+a_i+\epsilon_i$ ，where $f\sim N(\bar{f},\sigma_{f}^2)$ ; $a_i\sim N(\bar{a_{i}},\sigma_{a}^2)$; $\epsilon_i\sim N(0,\sigma_{\epsilon}^2)$. We can see the signals $\{z_i\}$ where $i\subseteq {1,...
- 11
1
vote
0
answers
168
views
Curvature of randomly generated B-spline curve
I am working on Bayesian statistical estimation of parameters (control points) of closed B-spline curve bounding an object on a an image. The problem is that I require those curves to not be much &...
- 41
2
votes
1
answer
274
views
Derive equation for regularized logistic regression with batch updates
I am trying to understand this paper by Chapelle and Li "An Empirical Evaluation of Thompson Sampling" (2011). In particular, I am failing to derive the equations in algorithm 3 (page 6). ...
- 59
2
votes
0
answers
84
views
Concentration of posterior probability around a tiny fraction of the prior volume
In the context of approximating the evidence $Z$ in a Bayesian inference setting
$$
Z = \int d\theta \mathcal L (\theta)\pi (\theta)
$$
with $\mathcal L$ the likelihood, $\pi$ the prior, John Skilling'...
- 21
1
vote
1
answer
170
views
Bayesian inverse problems on non-separable Banach spaces
I am now studying Bayesian inverse problems. In the note of Dashti and Stuart https://arxiv.org/abs/1302.6989, they mentioned that "... when considering a non-separable Banach space $B$, it is ...
- 41
1
vote
1
answer
215
views
Conditional Gaussians in infinite dimensions
I asked this over on cross validated, but thought it might also get an answer here:
The law of the conditional Gaussian distribution (the mean and covariance) are frequently mentioned to extend to the ...
- 379
4
votes
2
answers
254
views
Do these distributions have a name already?
In playing with some math finance stuff I ran into the following distribution and I was curious if someone had a name for it or has studied it or worked with it already.
To start, let $\Delta^n$ be ...
- 646
3
votes
1
answer
313
views
A quantity associated to a probability measure space
Let $(S,P)$ be a (finite) probability space. We associate to $(S,P)$ a quantity $n(S,P)$ as follows:
The probability of two randomly chosen events $A,B\subset S$ being independent is denoted by $n(S,P)...
- 330
4
votes
1
answer
673
views
Gaussian process kernel parameter tuning
I am reading on gaussian processes and there are multiple resources that say how the parameters of the prior (kernel, mean) can be fitted based on data,specifically by choosing those that maximize the ...
- 141
1
vote
0
answers
66
views
Estimation of probability matrix from samples at different time intervals
I am given discrete-time Markov chain that evolves on a finite subset $\{1,\dots,n\}$. This Markov chain is time-homogeneous and has a transition matrix $P$ that I want to estimate.
Let $X_t$ be the ...
- 562
0
votes
1
answer
229
views
CLT for random variables with positive support (e.g. exponential)
I have a bunch of iid $\{X_i\}$ with $X_i \sim \exp(\lambda)$ - let's say $\lambda = 1$. Now, classic version of CLT tells me:
\begin{equation}
\sqrt{n}\left(1-\bar{X}_n\right) \rightarrow \mathcal{N}\...
- 73
1
vote
0
answers
95
views
2d interpolation minimizing the integral of the norm of the Hessian
It is well known that cubic interpolation is the solution of the interpolation problem that minimizes the integral of the square of the second derivative:
$$ min_{f \text{ s.t. } f(x_i)=y_i} \int (f''(...
- 437
0
votes
1
answer
230
views
Lower bound for reduced variance after conditioning
Let $X$ be a random variable with variance $\tau^2$ and $Y$ be another random variable such that $Y-X$ is independent of $X$ and has mean zero and variance $\sigma^2$. (One can think of $Y$ as a noisy ...
- 3
-1
votes
1
answer
88
views
Linear operator over a simplex space in a multinomial distribution parameter estimation problem
This is actually a variant of a well-known problem of how the parameters of a multinomial distribution can be estimated by maximum likelihood, and this arises from a final year project I undertook ...
- 1
1
vote
1
answer
414
views
Posterior expected value for squared Fourier coefficients of random Boolean function
Let $f : \{0, 1\}^{n} \rightarrow \{-1, 1\}$ be a Boolean function. Let the Fourier coefficients of this function be given by
$$ \hat f(z) = \frac{1}{2^{n}} \sum_{x \in \{0, 1\}^{n}} f(x)(-1)^{x \cdot ...
1
vote
0
answers
96
views
Bayesian inference of stochastically evolving model parameters
I have a question related to self-calibration in radio interferometry, but I will try to phrase it as generic as possible. I have a set of data points, $D = \{ d_{0, t_0}, d_{1, t_0}, ..., d_{M, t_0}, ...
- 29
1
vote
1
answer
2k
views
Convolution of two Gaussian mixture model
Suppose I have two independent random variables $X$, $Y$, each modeled by the Gaussian mixture model (GMM). That is,
$$
f(x)=\sum _{k=1}^K \pi _k \mathcal{N}\left(x|\mu _k,\sigma _k\right)
$$
$$
g(y)=\...
- 51
3
votes
0
answers
234
views
Minimizing an f-divergence and Jeffrey's Rule
My question is about f-divergences and Richard Jeffrey's (1965) rule for updating probabilities in the light of partial information.
The set-up:
Let $p: \mathcal{F} \rightarrow [0,1]$ be a ...
- 101
1
vote
0
answers
87
views
Quantitative bounds on convergence of Bayesian posterior
Let $Y$ be a random variable in $[0,1]$, and let $X_1, X_2, \ldots$ be a sequence of random variables in $[0,1]$. Suppose that the $X_i$'s are conditionally i.i.d given $Y$ ; in other words, I'd like ...
- 173
2
votes
0
answers
123
views
Is there any good reference on the Bayesian view that can be helpful for reading papers on the number theory using heuristic arguments?
Nowadays there are many papers on the number theory using heuristics.
I have read some of them.
But I have no clear understanding of the Bayesian Probability(subjective probability).
The concept of ...
- 1,143
4
votes
0
answers
256
views
Convergence of the expectation of a random variable when conditioned on its sum with another, independent but not identically distributed
Suppose that for all $n \in \mathbf{N}$, $X_n$ and $Y_n$ are independent random variables with
$$X_n \sim \mathtt{Binomial}(n,1-q),$$
and
$$Y_n \sim \mathtt{Poisson}(n(q+\epsilon_n)),$$
where $q \in (...
- 91
-1
votes
1
answer
456
views
Proving the existence of a symmetric Bayesian Nash equilibrium
I am currently faced with the following question:
Consider the public goods game. Suppose that there are $I > 2$ players and that
the public goods is supplied (with benefit of 1 for all players) ...
- 29
0
votes
0
answers
71
views
restriction of a formula with matrix inverse multiplied by a vector
I'm trying to reproduce a proof from this paper but I'm stuck in one point (Lemma 6). The general subject is bayesian model for multi-armed bandit problem solved with Thompson sampling.
I think I ...
- 1
1
vote
0
answers
78
views
Bayesian posterior consistency when prior distribution is induced by a diffusion
Let $\Pi_{b,\sigma}$ be a prior distribution on $\{z_t\}_{t<T}\in C_0[0,T]$ induced by the following diffusion:
\begin{align}
d\tilde z_t&=b(\tilde z_t,t)dt+\sigma(\tilde z_t,t) dW_t, ~...
- 11
2
votes
0
answers
151
views
Convergence of Bayesian posterior
Let $\Delta [0,1]$ denote the set of all probability distributions on the unit interval.
Let $\mu \in \Delta [0,1]$ denote an arbitrary prior. Importantly, $\mu$ does not necessarily admit a density ...
- 131
1
vote
1
answer
172
views
Conditional density for random effects prediction in GLMM
I am currently working on generalized linear mixed models (GLMM) and need some help concerning the prediction of the random effects. More specifically, I don't understand the given representation of ...
- 13
0
votes
1
answer
532
views
Optimal solution to cross entropy loss in the continuous case
This could be a simple question but I don't have a satisfying answer.
Setup. Suppose that we have $K$ different classes, and consider cross entropy loss which maps a probability vector in the ...
- 143
9
votes
3
answers
565
views
What does the KL being symmetric tell us about the distributions?
Suppose two probability density functions, $p$ and $q$, such that $\text{KL}(q||p) = \text{KL}(p||q) \neq 0$. Intuitively, does that tell us anything interesting about the nature of these densities?
- 123
3
votes
0
answers
97
views
Have stick-breaking priors with non-iid atoms been considered, and if not, why not?
Roughly speaking, a stick-breaking prior is a random discrete probability measure $P$ on a measurable space $\mathcal X$ of the form
$$P=\sum_{j\ge1}w_j\delta_{\theta_j}$$
where $(w_j)_{j\ge1}$ is a ...
- 31
3
votes
1
answer
2k
views
Bayesian Inference with Student-t likelihood
Suppose I've observed $x$ from a Student-t distribution with unknown $\mu$, and I'd now like to infer $\mu$. Since the t-distribution isn't exponential family, there's no conjugate prior available, ...
- 41
5
votes
1
answer
431
views
Bounding the sensitivity of a posterior mean to changes in a single data point
There is a real-valued random variable $R$. Define a finite set of random variables ("data points") $$X_i = R + Z_i \; \text{for } i\in\{1,\ldots,n\},$$ where $Z_i$ are identically and independently ...
- 1,098
4
votes
0
answers
760
views
Bayesian Networks and Polytree
I am a bit puzzled by the use of polytree to infer a posterior in a Bayesian Network (BN).
BN are defined as directed acyclic graphs. A polytree is DAG whose underlying undirected graph is a tree. ...
- 41
3
votes
2
answers
768
views
Parametrising a sparse orthogonal matrix
I need to find a way to parametrise a matrix that is both sparse (to some degree) and orthogonal, i.e., I am looking for a parametrisation that describes $A \in \mathbb{R}^{n\times m}$ such that $AA^𝑇...
- 123
1
vote
0
answers
495
views
Gaussian Integrals over Spheres
I'm after a reference for an integral. In particular, I am looking a way to approximate or calculate the following:
$$ \int \limits_{\| \theta \|_2 = 1} e^{(-(\theta - \mu)^T \Sigma (\theta - \mu))} ...
- 11
1
vote
1
answer
166
views
The expectation of binary logistics regression with respect to Gaussian distribution
I am trying to compute the expectation of $g(s,x)=s \ln \sigma(x)+(1-s)\ln(1-\sigma(x))$ with respect to the normal distribution $\mathcal{N}(x;m,v)$, where we have $\sigma(x)=\frac{1}{1+e^{-x}}$. If ...
- 37
3
votes
1
answer
830
views
Bayesian methods in online setting
Imagine the following (very concrete) model: We have a series of random variables $x_k$ with values in $\lbrace 0, 1\rbrace$. We assume $x_k \mid p_k \sim \operatorname{Alt}(p_k),$ where $p_0 \sim R(0,...
- 161