Questions tagged [probabilistic-programming]

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Simple question regarding programming in WinBUGS/OpenBUGS [closed]

It seems very easy, but after so many attempts, I almost gave up. Basically, in WinBUGS/OpenBUGs, I want to sample from a normal distribution with mean 0 and variance 100, and store the iteration ...
Jacob's user avatar
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Using MCMC-derived posterior to design variational approximation function

I am trying to fit a hierarchical model that estimates the covariance of some parameters, using the probabilistic programming language pyro. In simulation experiments, I saw that the MCMC generates ...
David Shor's user avatar
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Error in accuracy test [duplicate]

This is an update from my previous question. I'll put my Model Development code here for your reference: ...
Amsyar Nifail's user avatar
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19 views

Probability of both M and N consecutive head runs in K independent tails terminated coin toss runs

Let's say I repeatedly toss a coin each day, until I get tails which terminates each run. I do that 1999 times (1999 runs). I count how many heads I got each run. 0, 1, 0, 3, 0, 2, 1, 5, 0 etc. What ...
TypicalHog's user avatar
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Process Modelling with LSTMs vs Probabilistic Programming

I am trying to model an aircraft’s turnaround process from the beginning (in-block) to the end (off-block). The goal is to gain transparency about the progress of the process / subprocesses and to ...
alex's user avatar
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How to structure Bayesian model for hiring data based on race, performance, and years of experience

I'm working on an analysis of some HR data that is attempting to answer the following question: Do applicants of different races have substantially different probabilities of being selected? For now, ...
mthelm85's user avatar
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27 views

Best way to show one Bayesian model is more certain and accurate than another, based on simulated data?

I'm trying to compare performance of two bayesian models $A$ and $B$ on simulated data. It's a recruitment curve fitting problem and I'm interested in how accurate these models are in estimating only ...
chesslad's user avatar
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Which is the best way to implement variational inference?

To implement variational inference in a Bayesian model, one essentially has the choice between different approaches that differ in their degree of automation and flexibility: manually deriving update ...
flcello's user avatar
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Book suggestion probabilistic programming

I like how pyro from Uber is structured, that it uses pytorch and how many features it brings. Pymc looks ok as well (but would not be my favorite with regards to the syntax and lifetime!) Do you have ...
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how to compute two bottom-up evaluations for discriminative Sum-Product Networks with Marginal Inference?

I am struggling with the two bottom-up evaluations for Sum-Product Networks with Discriminative Training with Marginal inference. In the paper "Discriminative Learning of Sum-Product Networks&...
Utkarsh Kathuria's user avatar
2 votes
1 answer
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Help Deriving Likelihood Term When the Target is Known Probabilistically

I am trying to model data $\{Y_t,Q_t\}_{t=1}^T$, where the model is parameterized by $\theta$. $Y_t$ is a quantity where the model prediction can be solved in closed form, $\hat{Y}_t(\theta)$, where ...
hipHopMetropolisHastings's user avatar
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How to interpret rank bar plot of a MCMC trace?

I am learning how to use PyMC for Bayesian inference. I coded up a random intercept $Y = \gamma + \sum_{j=1}^3 \beta_j \mathbb{I}_j + \epsilon$ and looked at the trace plots. Here is a ...
Galen's user avatar
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Question on the interpretation of the predicted results

I would like to understand something that is bothering me. Suppose you have a dataset and a probabilistic model that depends on few parameters $\alpha, \beta $ etc, and suppose that you find an ...
Alucard's user avatar
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In what ways do conjugate priors compose?

A lot of conjugate priors are known for a lot of likelihood distributions (mostly the exponential family). But most Bayesian models in practice don't just consist of one distribution. Usually, you ...
Turion's user avatar
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Generating a truncated normal distribution

I want to write code, say in Python to generate a truncated normal distributed random variable on the interval $[a,b]$. I have a standard function, call it N which will generate a normally distributed ...
Bob's user avatar
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2 answers
166 views

Conditioning of join gaussian over a line

I need to compute the conditional probability of bivariate normal distribution over a line. Let's suppose that X and Y both are normal distributions and that they are independent. Let's suppose that ...
sam's user avatar
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1 answer
962 views

Acceptance-Rejection Technique Theorem Proof

I am assigned to discuss the acceptance-rejection technique in our class. I have trouble understanding the last part of proving its theorem. The theorem goes like this: The acceptance-rejection ...
lil denise's user avatar
2 votes
4 answers
424 views

Estimate negative binomial dispersion parameter $k$ using mean and proportion of zeros

I came across supplemental methods of a paper estimating the mean ($R$) and dispersion ($k$) of a negative binomial distribution that stated: Page 8: "Given estimates of the mean ($\hat{R}$) and ...
jpsmith's user avatar
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Probability of drawing N elements from M elements with per element draw probability without replacement

I have a list of M elements with per element draw probability, summing to 1.0. Example (M=10): L = [0.3, 0.2, 0.01, 0.05, 0.02, 0.02, 0.2, 0.05, 0.05, 0.1] Now I ...
CreeperPower storing's user avatar
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probabilistic regression in existing packages

I know that typical machine-learning packages (such as scikit-learn or TensorFlow) contain plenty of functions for probabilistic classification: given a training set of pairs $(x_i,y_i)$, where $y_i$ ...
Vlad's user avatar
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Uncertainty Estimation with Bayesian Inference

I am modeling a generalized extreme value distribution with the code below in PYMC3. I have defined my own distribution as the gev is still not available in pymc3. The function defined is the PDF of ...
Dawar's user avatar
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1 vote
1 answer
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Regarding Gibbs sampling and HMC in fitting Bayesian model, their differences and advantages

I have a question regarding the two MCMC algorithms, Gibbs sampling and Hamiltonian Monte Carlo (HMC) for performing the Bayesian analysis. If using Gibbs sampling, my understanding is that we need to ...
user3269's user avatar
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1 vote
1 answer
263 views

Observation dependency in pymc3 models

I have a model, which can be simplified conceptually to: $$ a \sim TruncNormal(\mu = 1.0, \sigma=0.01, min = 0.9, max = 1.1)$$ $$y = a \cdot sin(b) $$ I can make observations about $y$, but these ...
50k4's user avatar
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Calculate the probability $P(S^2_{n-1}<s^2_{n-1})$ by Monte-Carlo simulation of the population distribution

i am struggling with the following problem, i have posted my attempt below, i am apparently supposed to get an answer of 0.632 with a random seed of 25 however i get a 1 as the answer. Please help i ...
HappyFeet's user avatar
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1 answer
333 views

Probabilistic Record Linkage [duplicate]

Record linkage is the task of identifying which records from different data sources refer to the same entities. Without the common identification key among different databases, this task could be ...
omar boukherys's user avatar
1 vote
0 answers
122 views

Can I use AdaBoost Regressor with Gaussian Process Regressor as base estimator, as my Surrogate Function?

I am using a text-extraction model whose hyperparamters I want to optimise. The model takes time(on average 1 hr) for training on the dataset. The algorithm I am using for hyperparameter tuning, is ...
supratim saha's user avatar
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1 answer
179 views

Is there a way to supress p-values in tbl_regression function in R? [closed]

I want to make tables including exponentiated parameter coefficients with CIs, but not with p-values. Right now i am using : ...
tcvdb1992's user avatar
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How do I fit a multiple linear regression where I know the relationship between some of the coefficients

I want to fit a linear regression with multiple independent variables. Say that I know that the coefficient of one variable is twice as large as the coefficient of another variable, or that one ...
gustavaman's user avatar
1 vote
0 answers
79 views

What does it mean to create predictions on predicted probabilities?

Background: I have a particular dataset of presence and absence of a specific bird. This data went through various means of simplification for machine learning techniques, for example random forests ...
Meilton's user avatar
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How do you write factor operations for rule-based conditional probability distributions?

Supposing I have rule-based conditional probability distributions (CPDs), $\{P(X|\text{Pa}_{X}), \cdots\}$, in a graphical model each represented as a set of rules $\mathscr{R}$ such that: For each ...
myselfesteem's user avatar
2 votes
1 answer
183 views

How to implement conditional probability distribution on set-valued Random variables?

A Random Set is a set-valued RV, i.e. a map $X:\Omega\to\mathcal{C}$ from a probability space $(\Omega,\Lambda,P)$ to the family of measurable closed sets $\mathcal{C}$ on a $\sigma-$algebra $\Lambda$ ...
Nacho's user avatar
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0 answers
65 views

Bayesian meta-analysis of multiple ranked lists?

Let's say I go around and ask a bunch of my friends to rank 30 movies. Each one returns me a list. Now the obvious treatment is to average the rankings, but I'm wondering if anyone has seen a more ...
thecity2's user avatar
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What is the benefit of adding control flow to probabilistic programming?

I was watching an interesting video on the Pyro package in Pytorch for probabilistic programming. One of the things that they ...
krishnab's user avatar
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1 vote
1 answer
271 views

Are nodes outside the markov blanket unconditionally independent?

Apologies if my question is deeply flawed, I've been working through a lot of material in the past few weeks and have a few blind spots here and there. On one level my question is this - given a ...
Kyle Pena's user avatar
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If given a table of samples from a multivariate distribution, can you get multivariate marginals by just extracting the columns you are interested in?

If we let $p(x)$ be the joint probability density where of r.v. $x$ where $x \in \mathbb{R}^{10}$ I am interested in looking at the joint marginal $p_{x_3, x_4, x_5}(x_3, x_4, x_5)$ I know that ...
Sean K's user avatar
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1 vote
1 answer
189 views

What are the limmitations of reparametrization gradients for discrete random variables? (Gumbel-softmax)

We know that one approach for re-parametrizing gradients for variational inference is taking the Gumbel-softmax estimator proposed in [1] and [2]. In [3], that is a talk on SVI, D. Blei at around 29:...
user1941126's user avatar
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0 answers
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Plotting a probability density based on the ratio of two random variables in PyMC3

I am trying to graphically represent the ratio of two probability distributions: $$f(x) = \frac{1}{\sqrt{2\pi}} \exp{(\frac{(-x)^2}{2})}$$ $$g(x) = \frac{1}{2} \exp{(-|x|)}$$ I'm a bit confused about ...
jbpib27's user avatar
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6 votes
1 answer
101 views

Statistical relationship between the stages of a stochastic optimization problem

What exactly do the "stages" of a stochastic program say about the statistical relationship between the problem variables? From what I understand, the stages imply both an "ordering&...
Amelio Vazquez-Reina's user avatar
1 vote
1 answer
264 views

REINFORCE for VAE Implementation Question

I want to compute the VAE loss through REINFORCE since my model's decoder is a deterministic program and is non-differentiable. The only REINFORCE implementation for VAE I was able to find used the ...
ADSBJason's user avatar
  • 139
3 votes
1 answer
135 views

Program analysis of a coin toss simulator

Here's a problem I'm trying to solve (it's a problem from the CS109 course, assignment 2): Suppose we want to write an algorithm fairRandom for randomly ...
Shirish Kulhari's user avatar
3 votes
0 answers
4k views

Variance of evidence lower bound(ELBO) loss function

When using Bayesian optimisation in a neural network our loss function is equal to: Here the first term is the KL divergence between the approximate and true posteriors. The second term is the ...
Onno Van Steen's user avatar
2 votes
1 answer
799 views

MAP of Gaussian Process Classification in Tensorflow Probability

I'm attempting to implement Gaussian Process Classification learning in tensorflow-probability, but my estimator turns out to be very biased toward zero. As opposed ...
tmrlvi's user avatar
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0 votes
1 answer
90 views

Unique game problem (ML, DP, PP etc) [closed]

Looking for a solution to my below game problem. I believe it to be some sort of dynamic programming, machine learning, or probabilistic programming challenge, but am unsure... This is my original ...
Michael Ramos's user avatar
2 votes
0 answers
400 views

How to get quantiles/probabilities of time series forecasts?

my problem is as follows : I am creating demand forecasts for some goods with different methods (ARIMA, ETS,..) The issue is that I would like to forecast the probabilities of those forecasts since ...
Nathan Furnal's user avatar
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1 answer
34 views

applied papers on probabilistic generative models and inference engines

I am looking for applications papers where people choose some task on which they will do Bayesian inferencing and graphical modeling, and then build an inference engine to infer latent parameters. And ...
user_1_1_1's user avatar
4 votes
1 answer
342 views

What property must a RNG have to be used in Monte Carlo simulations?

For example, every cryptographically secure pseudo-random number generator (CSPRNG) is required to satisfy the next-bit test and withstand "state compromise extensions". This makes me wonder what ...
nalzok's user avatar
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0 votes
1 answer
498 views

How to validate your loss function when it is not a simple regression or classification?

Assuming I have loss function f(y_pred,y_target) that I will use to train my neural network. In this case the loss function is a regression, and let's say it should ...
Kev1n91's user avatar
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2 votes
1 answer
755 views

Examples of applications of Markov random fields to data with a small number of variables

I am learning about some of the common applications of Markov random fields (a.k.a. undirected graphical models) to data science. A common feature of many applications I have read about is that the ...
Jeremy Lane's user avatar
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1 answer
383 views

What is the connection between Bayesian Networks and the models done in Probabilistic Programming?

For what I’ve read about them, they are very similar. Both model probabilistic models in which the nodes are random variables and the edges are dependencies among them. However, the literature I’ve ...
Lay González's user avatar
3 votes
1 answer
948 views

Plate notation for a hierarchical regression model (bayesian)

I've been recently studying hierarchical bayesian regressio (with pymc3), and I was wondering, how does the following example: http://twiecki.github.io/blog/2014/03/17/bayesian-glms-3/ look like ...
sdgaw erzswer's user avatar