cpfiffer

# Set up environment
import Pkg; Pkg.activate(".")

# Imports
using Distributions
using Plots
using Polynomials
using Optim
using ForwardDiff

cpfiffer

Formula:
returns ~ BITX + lag_returns + log_active + lag_active + log_avg_size + log_med_size + log_mode_size + native_transactions + :(fe(yearmonth))

                                  Fixed Effect Model                                  
=======================================================================================
Number of obs:                        1066   Degrees of freedom:                     44
R2:                                  0.157   R2 Adjusted:                         0.122
F Statistic:                       12.4004   p-value:                             0.000
R2 within:                           0.106   Iterations:                              1
Converged:                            true   

cpfiffer

using Turing, MCMCChains
using AbstractMCMC
using JLD2, FileIO

import Random: GLOBAL_RNG

# Create a model.
@model model(y) = begin
    μ ~ Normal(0, 1)
    s ~ InverseGamma(2,3)

cpfiffer

using Turing
using CSVFiles
using DataFrames
using Dates
using StatsPlots

# The TV syntax allows sampling to be type-stable.
@model garch(r, ::Type{TV}=Vector{Float64}) where {TV} = begin
    T = length(r)

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using MCMCChains

function sim(nsamples)
    cnt = 0
    for i in 1:1000
        chn = Chains(randn(nsamples,1,1))
        ess = describe(chn)[1].df[:ess][1]
        ess > nsamples ? cnt+=1 : nothing 
    end 
    return cnt 

cpfiffer

using CmdStan, StatsPlots, Random, MCMCDiagnostics

Random.seed!(12395391)

ProjDir = @__DIR__
cd(ProjDir)

berstanmodel = "
data { 
  int<lower=0> N; 

cpfiffer

using CmdStan, DynamicHMC
using StatsPlots, Random, MCMCDiagnostics
using Revise
using Turing, AdvancedHMC; const AHMC = AdvancedHMC
Random.seed!(1239911)

ProjDir = @__DIR__
cd(ProjDir)

Nsamples = 2000

cpfiffer

module P21 (properDivisors, sumDivisors, sumAmicable, isAmicable, amicableList) where



properDivisors :: Int -> [Int]
properDivisors x = [xs | xs <- [1..x-1], x `mod` xs == 0]

sumDivisors :: Int -> Int
sumDivisors x = sum $ properDivisors x

cpfiffer

Keybase proof

I hereby claim:

• I am cpfiffer on github.
• I am cpfiffer (https://keybase.io/cpfiffer) on keybase.
• I have a public key ASBuOyV4zsOUIxWVtBtAh0aRwqkKu25BwWPjUIxgfL8R4go

To claim this, I am signing this object:

cpfiffer

# From Keith Wynroe, a battle for the tokens:
#
# You have one token, and I have two tokens. Naturally, we both crave more tokens, so we play a game of skill that unfolds over a number of rounds in which the winner of each round gets to steal one token from the loser. The game itself ends when one of us is out of tokens — that person loses. Suppose that you’re better than me at this game and that you win each round two-thirds of the time and lose one-third of the time.
#
# What is your probability of winning the game?

@everywhere function play_game()
    p1 = 2
    p2 = 1
    play = true