hyunjimoon

import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
from collections import namedtuple

Decision = namedtuple('Decision', ['cable', 'assembly', 'pack'])
EquityRate = namedtuple('EquityRate', ['CEO', 'VC', 'Emp', 'Supp'])

INITIAL_MONEY = 760_000

hyunjimoon

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Constants
x1, x2, y1, y2 = 1, 3, 2, 4

# Grid of x, y points
x = np.linspace(0, 5, 400)
y = np.linspace(0, 5, 400)

hyunjimoon

# Two-Page Summary: Analogy between Bayesian Modeling and Startup Growth

## Introduction
The conceptual strategic operation of startups can be likened to Bayesian computation, particularly in the context of Simulation-Based Calibration Checking. This paper aims to draw an analogy between the P, A, D components in Bayesian modeling and startup growth, extending it to P, PD, PA, PAD models in both domains. We also discuss the most likely growth bottleneck sequences for startups, drawing parallels with Bayesian workflows.

## P, A, D Components and Models in Bayesian Workflow and Startup Growth

### 1. P, A, D component in Bayesian modeling
- **P**: Joint probability distribution over variables (e.g., p(y,θ))
- **A**: Posterior approximator (e.g., MCMC, VI)

hyunjimoon

functions {
  vector diagSPD_EQ(real alpha, real rho, real L, int M) {
    return sqrt((alpha^2) * sqrt(2*pi()) * rho * exp(-0.5*(rho*pi()/2/L)^2 * linspaced_vector(M, 1, M)^2));
  }
  vector diagSPD_periodic(real alpha, real rho, int M) {
    real a = 1/rho^2;
    int one_to_M[M];
    for (m in 1:M) one_to_M[m] = m;

hyunjimoon

Cruise, Tom		'Minority Report': The Players (2002) (V)  [Himself]
			'Minority Report': The Story, the Debate (2002) (V)  [Himself]
			'Rain Man' Featurette (1988) (TV)  [Himself]  <3>
			'War of the Worlds': Characters - The Family Unit (2005) (V)  [Himself]
			'War of the Worlds': Production Diaries, East Coast - Beginning (2005) (V)  [Himself]
			'War of the Worlds': Production Diaries, East Coast - Exile (2005) (V)  [Himself]
			'War of the Worlds': Production Diaries, West Coast - Destruction (2005) (V)  [Himself]
			'War of the Worlds': Production Diaries, West Coast - War (2005) (V)  [Himself]
			'War of the Worlds': Revisiting the Invasion (2005) (V)  [Himself]
			100 Years at the Movies (1994) (TV)  (archive footage)  [Himself]

hyunjimoon

import xarray as xr

def gen_param_names():
# spatial subscript
    state_names = ['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California', 'Colorado', 'Connecticut', 'Delaware',
                   'Florida', 'Georgia', 'Hawaii', 'Idaho', 'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky',
                   'Louisiana', 'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi', 'Missouri',
                   'Montana', 'Nebraska', 'Nevada', 'New Hampshire', 'New Jersey', 'New Mexico', 'New York',
                   'North Carolina', 'North Dakota', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', 'Rhode Island',
                   'South Carolina', 'South Dakota', 'Tennessee', 'Texas', 'Utah', 'Vermont', 'Virginia', 'Washington',

hyunjimoon

import pandas
import scipy 

# 49MB
df = pd.read_csv("oldtitle.csv")

# takes 1s to create pkl, 51MB
df.to_pickle("oldtitle.pkl")

# takes 1.5s to create nc, 610MB

hyunjimoon

for section in am.sections:
    for element in section.elements:
        print("*" * 10)
        print(f"name: {element.name}")
        print(f"length: {len(element.components)}")
        for component in element.components:
            print(f"type: {component.type}")
            print(f"subtype: {component.subtype}")
            print(f"subscript: {component.subscripts}")
            print(component.ast)