coppeliaMLA

digraph PurchaseFlow {
    rankdir=TD;
    edge [color="#333333", penwidth=1, arrowsize=1.2, arrowhead=onormal, style="dashed"];
    splines=spline;
    node [shape=box];
    nodesep=0.8;    // Increase horizontal spacing between nodes
    ranksep=0.7;
    // Node definitions with labels
    A [label="available funds"];
    B [label="purchases product"];

coppeliaMLA

graph LR
    A[Tensorflow] --implements--> B[Deep neural nets]
    A --was developed by --> C[Google]

graph LR

coppeliaMLA

coppeliaMLA

coppeliaMLA

coppeliaMLA

graph LR
    A((Keras 3)) --is a high level API for --> B((Tensorflow))
    B --was developed by-->H[Google]
    C((Pytorch)) --was developed by-->D[Meta]
    A --is a high level API for --> C
    A --is a high level API for --> F[JAX]
    F --was developed by-->H
    A --implements--> G[Deep neural nets]
 B --implements--> G

coppeliaMLA

coppeliaMLA

import numpy as np
import scipy.stats as stats

# Number of trials and successes for each group
n_1 = 100  
x_1 = 6   
n_2 = 125 
x_2 = 5    

# Parameters for the prior distributions

coppeliaMLA

coppeliaMLA

modBin<-function(k, n, p){
  
  if (k<=n) {
    return(dbinom(k, n, p))
  }
  else {
    return(0)
  }
}