Publications by Vadim Sokolov (GMU), Joshua Auld (ANL) and Natalia Zuniga (ANL)
Gen Bayes Code Examples: COBAL-EBEB, 2024
Gen Bayes Code Examples: COBAL-EBEB, 2024 Nick Polson and Vadim Sokolov Quantile Neural Network for Synthetic Data Code import numpy as np import torch import matplotlib.pyplot as plt import scipy.stats # Sin n = 10000 # x = np.linspace(-1,1, n) np.random.seed(8) x = np.random.uniform(-1,1,(n)) x = np.sort(x) eps = np.random.normal(0,np.exp(1-...
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Deep Learning Tutorial
Deep Learning Tutorial: COBAL-EBEB, 2024 Introduction Review of deep learning methods which provide insight into structured high-dimensional data. Deep learning uses layers of semi-affine input transformations to provide a predictive rule. Applying these layers of transformations leads to a set of attributes (or, features) To which probabilistic ...
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Austin Sensitivity
Austin Sensitivity Analysis Author Vadim Sokolov Published July 2, 2024 Exploratory analysis X = load_json_training_data_x('data/timedep_training_data.json'); n = nrow(X); p = ncol(X) X = cbind(X,1:n) colnames(X)[p+1]="run_id" Y = read_csv('data/vmt_vht.csv') %>% select(million_VMT,million_VHT,speed_mph,count,run_id) X = as_tibble(X) d = i...
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Forecasting: From Structural Models to Tranformers
Forecasting: From Structural Models to Tranformers Author Vadim Sokolov Published November 19, 2023 This is rmarkdown version (with some aditoins) of the blog post by Steven L. Scott (2017), available at https://www.unofficialgoogledatascience.com/2017/07/fitting-bayesian-structural-time-series.html Introduction Time series data are everywhe...
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Forecasting: From Structural Models to Transformers
This is rmarkdown version (with some aditoins) of the blog post by Steven L. Scott (2017), available at https://www.unofficialgoogledatascience.com/2017/07/fitting-bayesian-structural-time-series.html Introduction Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. This post de...
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Hockey Shots
NHL Shots Oilers forward Zach Hyman had the most shots on goal (179) from the high-danger area, and Edmonton had the most shots on goal (932) and goals (178) from there as a team. Tampa Bay Lightning center Brayden Point scored the most goals (36). Anaheim Ducks goalie John Gibson faced the most shots on goal (641) and made the most saves (527) fro...
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Hockey Shots
NHL Shots Oilers forward Zach Hyman had the most shots on goal (179) from the high-danger area, and Edmonton had the most shots on goal (932) and goals (178) from there as a team. Tampa Bay Lightning center Brayden Point scored the most goals (36). Anaheim Ducks goalie John Gibson faced the most shots on goal (641) and made the most saves (527) fro...
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Austin Transit Sensitivity
2023-10-10 Overview Previous calibraiton efforts relied on updating values of constant parameters Model outputs are most sensitive to the constant parameters We were able to achieve good calibration metrics with this strategy Downside: only updating constants leads to overfitting. In this study: analyse sensitivity of transit-related behavioral p...
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Deep Learning Examples 2022
Simple Circle Example numSamples = 200 # total number of observations radius = 10 # radius of the outer curcle noise = 0.0001 # amount of noise to be added to the data d = matrix(0,ncol = 3, nrow = numSamples) # matrix to store our generated data # Generate positive points inside the circle. for (i in 1:(numSamples/2) ) { r = runif(1,0, radius...
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TNC Analysis for Chicago
Introduction We develop regression models for analyzing impact of the ride sharing services on transit demand. Our analysis is motivated by the problem of the loss of transit ridership over the past several years. Those losses overlap with the introduction of Transportation Network Companies (TNCs). There is a seeming correlation between the two,...
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