Using RStudio, combination of vector operation and linear regression

Using RStudio, combination of vector operation and linear regression

Using RStudio, combination of vector operation and linear regression 

Using RStudio, linear regression model example(least square method)

Using RStudio, linear regression model example(least square method)

Using RStudio, linear regression model example(least square method)

Using RStudio, t test example

Using RStudio, t test example

Using RStudio, t test example

Using RStudio, log axes example

Using RStudio, log axes example

Using RStudio, log axes example

Using RStudio, line segment example

Using RStudio, line segment example

Using RStudio, line segment example

Using RStudio, plotting example

Using RStudio, plotting example

Using RStudio, plotting example

Using julia, finding 97 multiples within 1000

Using julia, finding 97 multiples within 1000

Using julia, finding 97 multiples within 1000

Using julia, another functions example

Using julia, another functions example

 Using julia, another functions example

Using julia, function examples

Using julia, function examples

Using julia, function examples

Using julia, conditional evaluation example

Using julia, conditional evaluation example

Using julia, conditional evaluation example

Using julia, function definitions example

Using julia, function definitions example

Using julia, function definitions example

Using julia , arithmetic operations

Using julia , arithmetic operations

Using julia , arithmetic operations

Using julia, calculating complex numbers operation

Using julia, calculating complex numbers operation

Using julia, calculating complex numbers operation

Using R, performing grid.circle lib

Using R, performing grid.circle lib

Using R, performing grid.circle lib

Using R, performing grid.circle lib

Using R, plotting user-defined function graphs

Using R, plotting user-defined function graphs

Using R, plotting user-defined function graphs

Using Numpy(a python library), a system of equations is solved.(matrix format)

Using Numpy(a python library), a system of equations is solved.(matrix format)

Using R, appending a data into a vector

Using R, appending a data into a vector

Using R, appending a data into a vector

Using R, a few functions operating on entire vectors

Using R, a few functions operating on entire vectors

Using R, a few functions operating on entire vectors

Using Ipython Notebook, Bayesian Inference

Using Ipython Notebook, Bayesian Inference

In [8]:

%matplotlib inline

from IPython.core.pylabtools import figsize

import numpy as np

from matplotlib import pyplot as plt

figsize(11, 9)

import scipy.stats as stats

dist = stats.beta

n_trials = [0, 10, 12, 3, 4, 5, 18, 15, 50, 500]

data = stats.bernoulli.rvs(0.5, size=n_trials[-1])

x = np.linspace(0, 1, 100)

# For the already prepared, I'm using Binomial's conj. prior.

for k, N in enumerate(n_trials):

    sx = plt.subplot(len(n_trials) / 2, 2, k + 1)

    plt.xlabel("$p$, probability of heads") \

        if k in [0, len(n_trials) - 1] else None

    plt.setp(sx.get_yticklabels(), visible=False)

    heads = data[:N].sum()

    y = dist.pdf(x, 1 + heads, 1 + N - heads)

    plt.plot(x, y, label="observe %d tosses,\n %d heads" % (N, heads))

    plt.fill_between(x, 0, y, color="#348ABD", alpha=0.4)

    plt.vlines(0.5, 0, 4, color="k", linestyles="--", lw=1)

    leg = plt.legend()

    leg.get_frame().set_alpha(0.4)

    plt.autoscale(tight=True)

plt.suptitle("Bayesian updating of posterior probabilities",

             y=1.02,

             fontsize=14)

plt.tight_layout()

In [ ]: