Questions tagged [probabilistic-programming]
The probabilistic-programming tag has no usage guidance.
82
questions
-1
votes
0
answers
11
views
R package documentation namespace and description [closed]
Good morning,
I am writing an R package in a first draft I have imported the function corrplot from the package corrplot adding it also to the NAMESPACE (using roxygen2). That is:
...
- 1
4
votes
1
answer
161
views
Can probabilistic predictions be obtained from gradient boosting models CatBoost and XGBoost?
I'm looking for probabilistic predictions ($\Pr(Y\mid X=x)$) using CatBoost or XGBoost for a continuous target variable that is in [0, 1] (i.e., ratio). Can I use the official library to generate ...
- 145
0
votes
0
answers
23
views
PyMC model from aggregated data
I am starting with probabilistic programming and PyMC and seems that stuck with the first step. I am trying to figure out something very simple and then add complexity. Appreciate if you can help me ...
- 121
1
vote
0
answers
22
views
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 ...
- 31
0
votes
0
answers
9
views
Error in accuracy test [duplicate]
This is an update from my previous question.
I'll put my Model Development code here for your reference:
...
0
votes
0
answers
48
views
Probability of encountering certain numbers of consecutive heads over a number of tails terminated coin tossing runs
I'm tossing coins and recording the number of heads I get before I get tails, and I repeat that 1999 times (1999 tails terminated coin tossing runs).
...
0
votes
0
answers
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 ...
- 211
2
votes
1
answer
105
views
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 ...
- 21
5
votes
1
answer
326
views
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 ...
2
votes
1
answer
53
views
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 ...
6
votes
1
answer
549
views
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 ...
- 9,522
1
vote
1
answer
24
views
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 ...
- 325
2
votes
0
answers
38
views
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 ...
- 210
0
votes
0
answers
543
views
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 ...
- 115
3
votes
2
answers
308
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 ...
- 449
5
votes
1
answer
1k
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 ...
- 51
2
votes
4
answers
474
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 ...
- 329
1
vote
0
answers
69
views
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 ...
0
votes
0
answers
58
views
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 $y_i$ ...
- 1
0
votes
0
answers
97
views
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 ...
- 1
1
vote
1
answer
686
views
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 ...
- 5,222
1
vote
1
answer
275
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 ...
- 113
0
votes
1
answer
42
views
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 ...
- 113
1
vote
1
answer
388
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 ...
1
vote
0
answers
127
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 ...
0
votes
1
answer
203
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 :
...
- 139
0
votes
0
answers
29
views
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 ...
1
vote
0
answers
81
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 ...
- 141
0
votes
0
answers
28
views
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 ...
- 141
2
votes
1
answer
191
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 $\Lambda$ ...
- 153
1
vote
0
answers
68
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 ...
- 1,955
0
votes
0
answers
57
views
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 ...
- 1,532
1
vote
1
answer
285
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 ...
- 212
1
vote
0
answers
64
views
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 ...
- 33
1
vote
1
answer
199
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:...
- 111
0
votes
0
answers
72
views
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 ...
- 73
6
votes
1
answer
102
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&...
- 19.5k
1
vote
1
answer
302
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 ...
- 139
3
votes
1
answer
136
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 ...
- 750
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 ...
2
votes
1
answer
815
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 ...
- 1,057
0
votes
1
answer
91
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 ...
- 121
2
votes
0
answers
411
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 ...
- 151
0
votes
1
answer
35
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 ...
- 235
4
votes
1
answer
365
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 ...
- 1,797
1
vote
1
answer
528
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 ...
- 173
2
votes
1
answer
807
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 ...
- 121
0
votes
1
answer
385
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 ...
- 391
3
votes
1
answer
1k
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 ...
- 1,429
11
votes
2
answers
3k
views
Probabilistic programming vs "traditional" ML
I was browsing the github repo for Pymc and found this notebook:
Variational Inference: Bayesian Neural Networks
The author extols the virtues of bayesian/probabilistic programming but then goes on ...
- 874